Generally, the invention relates to surgical instruments. More specifically, the invention relates to method and system for automating usage analysis of surgical instruments.
Background
[002] Presently, during a surgery, variety and increasing complexity of surgical techniques used, with high number of surgical instruments employed, demands efficient techniques for tracking usage cycle of surgical instruments while performing a surgical process. Moreover, tracking of usage cycle of each of the surgical instruments is vital information for hospital staff to perform maintenance of surgical instruments and to make decisions for condemning or continuing usage of the surgical instruments. However, usually the surgical instruments required for performing the surgical processes are bundled in a form of one or more kits as per demand of a specific surgical process and remains in bundle throughout shipment process, sterilization process, and storage conditions.
[003] Moreover, these kits including the surgical instruments can be opened only in operation room during start of the surgical process. Once the surgical process is completed, the surgical instruments are put back in the kits as a same bundle after necessary cleaning, decontamination, and inspection procedures after the surgery process. Therefore, tracking information such as frequency of shipment and condition of each of the surgical instruments are available only for the bundle at any given point in time. Currently, many techniques exist for monitoring and tracking usage of surgical instruments, but none of the existing techniques are capable of capturing information about usage pattern, frequency of use, and usage duration of individual surgical instrument present in the kit required for performing the surgical process. Since, the information about usage pattern, frequency of use, and usage duration of individual surgical instrument is crucial as this captured information could open-up scope for

optimizations of design and kitting strategy of the surgical instruments, thereby reducing cost.
[004] Therefore, there is a need of implementing an efficient and reliable method and system for automating usage analysis of surgical instruments.
SUMMARY OF INVENTION
[005] In one embodiment, a method for automating usage analysis of surgical instruments is disclosed. The method may include capturing at least one of a set of images and a video stream of a plurality of surgical instruments placed on a table. It should be noted that, each of the set of images and the video stream is captured from initiation till completion of a surgical process. The method may include processing at least one of the set of images and the video stream. It should be noted that, the set of images and the video stream are processed via an Al model to create a data log associated with each of the plurality of surgical instruments. The method may include analyzing the data log to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments. The method may include comparing, for each of the at least one surgical instrument, each of the at least one usage attribute with associated thresholds. The method may include updating a database based on comparing each of the at least one usage attribute with the associated thresholds. The method may include notifying at least one user in response to the comparing.
[006] In another embodiment, a system for automating usage analysis of surgical instruments is disclosed. The system includes a processor and a memory communicatively coupled to the processor. The memory may store processor-executable instructions, which, on execution, may causes the processor to capture at least one of a set of images and a video stream of a plurality of surgical instruments placed on a table. It should be noted that, each of the set of images and the video stream is captured from initiation till completion of a surgical process. The processor-executable instructions, on execution, may further cause the processor to process at least one of the set of images and the video stream. It should be noted that, the set of images and the video stream are processed via an Al model to create a data log associated with each of the plurality of surgical instruments. The processor-executable instructions, on execution, may further cause the processor to analyze the data log to identify at least one usage attribute associated with at least one surgical instrument

from the plurality of surgical instruments. The processor-executable instructions, on execution, may further cause the processor to compare, for each of the at least one surgical instrument, each of the at least one usage attribute with associated thresholds. The processor-executable instructions, on execution, may further cause the processor to update a database based on comparing each of the at least one usage attribute with the associated thresholds. The processor-executable instructions, on execution, may further cause the processor to notify at least one user in response to the comparing.
[007] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The present application can be best understood by reference to the following description taken in conjunction with the accompanying drawing figures, in which like parts may be referred to by like numerals.
[009] FIG. 1 illustrates a functional diagram of a system for automating usage analysis of surgical instruments, in accordance with an embodiment.
[010] FIG. 2 illustrates a functional block diagram of an Artificial Intelligence (Al) model of an electronic device configured to automate usage analysis of surgical instruments, in accordance with an embodiment.
[011] FIG. 3 illustrates a flowchart of a method for automating usage analysis of surgical instruments, in accordance with an embodiment.
[012] FIG. 4 illustrates a flow diagram of a system for automating usage analysis of surgical instruments, in accordance with an exemplary embodiment.
[013] FIG. 5 illustrates a flowchart of a method for creating a data log, in accordance with an embodiment.
[014] FIG. 6 illustrates a flowchart of a method of updating a database, in accordance with an embodiment.
[015] FIG. 7 represents a table depicting an updated database file generated based on at least one usage attribute identified for at least one surgical instrument, in accordance with an exemplary embodiment.
[016] FIG. 8 illustrates a normal scenario of tracking usage of a plurality of surgical instruments, in accordance with an exemplary embodiment.

[017] FIG. 9 illustrates a scenario of automated usage analysis of each of a plurality of surgical instruments, in accordance with an exemplary embodiment.
DETAILED DESCRIPTION OF THE DRAWINGS
[018] The following description is presented to enable a person of ordinary skill in the art to make and use the invention and is provided in the context of particular applications and their requirements. Various modifications to the embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, in the following description, numerous details are set forth for the purpose of explanation. However, one of ordinary skill in the art will realize that the invention might be practiced without the use of these specific details. In other instances, well-known structures and devices are shown in block diagram form in order not to obscure the description of the invention with unnecessary detail. Thus, the invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
[019] While the invention is described in terms of particular examples and illustrative figures, those of ordinary skill in the art will recognize that the invention is not limited to the examples or figures described. Those skilled in the art will recognize that the operations of the various embodiments may be implemented using hardware, software, firmware, or combinations thereof, as appropriate. For example, some processes can be carried out using processors or other digital circuitry under the control of software, firmware, or hard-wired logic. (The term "logic" herein refers to fixed hardware, programmable logic and/or an appropriate combination thereof, as would be recognized by one skilled in the art to carry out the recited functions.) Software and firmware can be stored on computer-readable storage media. Some other processes can be implemented using analog circuitry, as is well known to one of ordinary skill in the art. Additionally, memory or other storage, as well as communication components, may be employed in embodiments of the invention.
[020] A functional diagram of a system 100 for automating usage analysis of surgical instruments is illustrated in FIG. 1, in accordance with an embodiment. The system 100 may be configured to automatically perform usage analysis of each of a plurality of surgical instruments placed on a surgical table for performing a specific

surgical process. The surgical table may be present in an operation room of a hospital. Moreover, the usage analysis of each of the plurality of surgical instruments placed on the surgical table may be performed from initiation till completion of the surgical process. In particular, in order to perform the automatic usage analysis of each of the plurality of surgical instruments, the system 100 may include an electronic device 102.
[021] The electronic device 102 may be configured to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments. In an embodiment, the at least one usage attribute associated with each of the plurality of surgical instruments may include, but is not limited to, at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need. Examples of the plurality of surgical instruments may include, but is not limited to, scissors, surgical blades, forceps, retractors, tapered needle, clamps, suctions, staplers and clips, and laparoscopic surgical instruments. In order to identify at least one usage attribute associated with the at least one surgical instrument, the electronic device 102 may include an Artificial intelligence (Al) model 104 and an in-built camera 106. In some embodiment, the electronic device 102 may include the Al model 104 as an edge implementation.
[022] Initially, in an embodiment, a camera may be configured to capture at least one of a set of images and a video stream of the plurality of surgical instruments placed on a table. The table may correspond to the surgical table placed inside the operation room of the hospital. Further, the camera used for capturing the at least one of the set of images and the video stream may correspond to the inbuilt camera 106 of the electronic device 102 or a camera of one of the plurality of external devices 124. Moreover, the camera may be any commodity camera capable of capturing each of the set of images and the video stream. Examples of the camera may include, but is not limited to, fixed-lens rangefinder camera, digital single-lens reflex (DSLR) camera, robotic-arm based camera, wearable camera, and a HoloLens.
[023] Based on at least one of the set of images and the video stream captured, the Al model 104 may be configured to process the at least one of the set of images and the video stream. Moreover, the set of images and the video stream may be processed via the Al model 104 in order to create a data log associated with each of the plurality of surgical instruments. A method of processing the at least one of the set of images and the video stream has been further explained in greater detail in conjunction to FIG.5. Once the data log is created, the Al model 104 may be configured

to analyze the data log. The Al model 104 may analyze the data log in order to identify the at least one usage attribute associated with the at least one surgical instrument from the plurality of surgical instruments.
[024] Based on identifying the at least one usage attribute, the Al model 104 may be configured to compare each of the at least one usage attribute identified for each of the at least one surgical instrument with an associated threshold. Further, based on comparing each of the at least one usage attribute with associated thresholds, the Al model 104 may be configured to update a database 116 connected to the electronic device 102. A method of updating the database 116 has been further explained in greater detail in conjunction to FIG. 6. In addition to updating the database 116, the Al model 104 may be configured to notify at least one user based on comparison of each of the at least one usage attribute identified for each of the at least one surgical instrument with the associated thresholds. This is further explained in detail in conjunction with FIG. 2 to FIG. 9.
[025] Examples of the electronic device 102 may include, but are not limited to, a server, a desktop, a laptop, a notebook, a tablet, a smartphone, a mobile phone, an application server, or the like. The electronic device 102 may further include a memory 108, a processor 110, and a display 112. The display 112 may further include the user interface 114. The user may interact with the electronic device 102 and vice versa through the display 112.
[001] The display 112 may be used to display results (i.e., the set of images captured, the video stream captured, the data log created, the at least one usage attribute identified, the set of relevant data captured, etc.) based on actions performed by the electronic device 102, to the user (i.e., a person working in surgical department of the hospital). Moreover, the display 112 may be used to display results of the comparison of each of the at least one usage attribute identified for each of the at least one surgical instrument with the associated thresholds. The result of the comparison may change based on the at least one usage attribute identified for a new surgical instrument detected in at least one of the set of images and the video stream captured. In addition, the display 112 may be used to display a notification send to the at least one user based on the comparison of each of the at least one usage attribute identified for each of the at least one surgical instrument with the associated thresholds.
[002] The user interface 114 may be also used by the at least one user to provide inputs to the electronic device 102. Thus, for example, in some embodiment,

the electronic device 102 may ingest an input that includes a user selection of one of the plurality of surgical instruments in order to extract at least one suggestive attribute associated with the one of the plurality of surgical instruments. Further, in some embodiments, the electronic device 102 may render intermediate results (e.g., the created data log, the at least one usage attribute identified, the set of relevant data captured) or final results (e.g., result of comparison of each of the at least one usage attribute associated with each of the at least one surgical instrument with the associated threshold and the at least one suggestive attribute extracted) to the user via the user interface 114.
[003] The memory 108 may store instructions that, when executed by the processor 110, may cause the processor 110 to perform automatic usage analysis of each of the plurality of surgical instruments. The processor 110 may perform automatic usage analysis of each of the plurality of surgical instruments captured via at least one of the set of images and the video stream, in accordance with some embodiments. As will be described in greater detail in conjunction with FIG. 2 to FIG. 9, in order to perform the automatic usage analysis of each of the plurality of surgical instruments, the processor 110 in conjunction with the memory 108 may perform various functions including capturing of at least one of the set of images and the video stream of the plurality of surgical instruments, processing of the at least one of the set of images and the video stream, analyzing of the data log, comparing of each of the at least one usage attribute with the associated thresholds, etc.
[004] The memory 108 may also store various data (e.g., the set of images captured, the video stream captured, the created data log, the at least one usage attribute identified, the set of relevant data captured, etc.) that may be captured, processed, and/or required by the electronic device 102. The memory 108 may be a non-volatile memory (e.g., flash memory, Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM) memory, etc.) or a volatile memory (e.g., Dynamic Random Access Memory (DRAM), Static Random-Access memory (SRAM), etc.).
[005] Further, the database 116 connected to the electronic device 102 may be used to store a plurality of profiles created for each of the plurality of surgical instruments, a plurality of surgeons, and a plurality of surgical procedures. In addition, the database 116 may store result of comparison of each of the at least one usage attribute associated with each of the at least one surgical instrument with the

associated thresholds. Additionally, the database 116 may be periodically updated based on identification of a new type of surgical process from a plurality of surgical processes.
[006] Further, the electronic device 102 may interact with a server 118 or external devices 124 over a network 122 for sending and receiving various data. The network 122, for example, may be any wired or wireless communication network and the examples may include, but may be not limited to, the Internet, Wireless Local Area Network (WLAN), Wi-Fi, Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), and General Packet Radio Service (GPRS).
[007] In some embodiment, the electronic device 102 may fetch information associated with the plurality of surgical instruments from the server 118. In addition, the server 118 may provide access of the information associated with the plurality of surgical instruments to the plurality of users. The server 118 may further include a database 120. The database 120 may store images of the plurality of surgical instruments. By way of an example, the database 120 may store images of the plurality of surgical instruments in order to identify each of the plurality of surgical instruments based on mapping of each of the set of images with one of the plurality of surgical instrument images. The database 120, may be periodically updated with a new surgical instrument identified in the set of images. Alternatively, the electronic device 102 may receive the set of images and the video stream from a camera of one of the external devices 124.
[008] Referring now to FIG. 2, a functional block diagram of an Al model of an electronic device configured to automate usage analysis of surgical instruments is illustrated, in accordance with an embodiment. In reference to FIG. 1, the Al model may correspond to the Al model 104 of the electronic device 102. Initially, at least one of the set of images and the video stream may be received as an input data 202 from the camera 106 by the Al model 104. The at least one of the set of images and the video stream may be of the plurality of surgical instruments placed on the table. Examples of the plurality of surgical instruments may include, but is not limited to, scissors, surgical blades, forceps, retractors, tapered needle, clamps, suctions, staplers and clips, orthopedic surgical instruments, and laparoscopic surgical instruments.
[009] In an embodiment, the Al model 104 may include a reception module 204, a processing module 206, an analysis module 208, and a comparison module

210. In addition to the above mentioned modules, the Al model 104 of the electronic device 102 may be connected to a database 212. The modules 204-210 may include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The modules 204-212 described herein may be implemented as software modules that may be executed in a cloud-based computing environment of the electronic device 102.
[010] The reception module 204 may be configured to receive the input data 202. In an embodiment, the input data 202 may include at least one of the set of images and the video stream captured. Each of the set of images and the video stream may be of the plurality of surgical instruments placed on the table. The table may correspond to the surgical table placed inside the operation room of the hospital. Moreover, at least one of the set of images and the video stream may be captured from initiation till completion of the surgical process. In other words, each of the set of images and the video stream may depict usage of each of the plurality of surgical instruments during the surgical process. As will be appreciated, in one embodiment, the reception module 204 may receive only the set of images as the input data 202. In another embodiment, the reception module 204 may receive only the video stream as the input data 202. In yet another embodiment, the reception module 204 may receive both the set of images and the video stream as the input data 202. Upon receiving at least one of the set of images and the video stream, the reception module 204 may be configured to send the received set of images and the video stream to the processing module 206.
[011] The processing module 206 may be configured to receive the set of images and the video stream of each of the plurality of surgical instruments from the reception module 204. Upon receiving at least one of the set of images and the video stream, the processing module 206 may be configured to process the at least one of the set of images and the video stream. The processing module 206 may process the set of images and the video stream in order to create the data log associated with each of the plurality of surgical instruments. A method of processing the set of images and the video stream in order to create the data log has been further explained in detail in conjunction to FIG. 5. Once the data log is created, the processing module 206 may be configured to send the data log to the analysis module 208.
[012] The analysis module 208 may be configured to receive the data log from the processing module 206. Upon receiving the data log, the analysis module 208 may

be configured to analyze the data log. The analysis module 208 may analyze the data log in order to identify at least one usage attribute. The at least one usage attribute identified may be associated with at least one surgical instrument from the plurality of surgical instruments. In an embodiment, the at least one usage attribute of each of the plurality of surgical instruments may include, but is not limited to, at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need. Once the at least one usage attribute associated with the at least one surgical instrument is identified, the analysis module 208 may be configured to send the at least one usage attribute associated with the at least one surgical instrument to the comparison module 210.
[013] The comparison module 210 may be configured to receive the at least one usage attribute associated with the at least one surgical instrument from the analysis module 208. Upon receiving the at least one usage attribute, the comparison module 210 may be configured to compare each of the at least one usage attribute identified for each of the at least one surgical instrument with associated thresholds. The thresholds associated with each of the at least one usage attribute may correspond to a pre-defined threshold for each of the at least one usage attribute. It should be noted that, the associated threshold for each of the at least one usage attribute may be user-defined. In an embodiment, each of the at least one usage attribute may be compared with the associated threshold in order to identify one or more of the at least one usage attribute associated with one or more of the plurality of surgical instrument with higher threshold value than the associated thresholds. The identification of the one or more of the at least one usage attribute having higher threshold value than the associated threshold may be done in order to notify at least one user about the one or more of the plurality of surgical instrument with higher threshold value than the associated thresholds. The at least one user may be notified in order to take a suitable action for the one or more of the plurality of surgical instrument with higher threshold value.
[014] Further, based on the comparison of each of the at least one usage attribute with the associated thresholds, the comparison module 210 may be configured to update the database 212. In order to update the database 212, the comparison module 210 may create a plurality of profiles for each of the plurality of surgical instruments, a plurality of surgeons, and a plurality of surgical procedures. In an embodiment, the comparison module 210 may create each of the plurality of

profiles in order to enable at least one user, i.e., (a person working in a surgical department of the hospital) to analyze each of the plurality of profiles created. The at least one user may analyze each of the plurality of profiles in order to extract at least one suggestive attribute associated with each of the plurality of surgical instruments. In reference to FIG. 1, the database 212 may correspond to the database 116.
[015] In particular, as will be appreciated by those of ordinary skill in the art, various modules 204-210 for performing the techniques and steps described herein may be implemented in the electronic device 102, either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the electronic device 102 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some, or all of the processes described herein may be included in the one or more processors on the host computing system. Even though FIGs. 1-2 describe the electronic device 102, the functionality of the components of the electronic device 102 may be implemented in any computing devices.
[016] Referring now to FIG. 3, a flowchart of a method for automating usage analysis of surgical instruments is illustrated, in accordance with an embodiment. At step 302, at least one of a set of images and a video stream of a plurality of surgical instruments may be captured. Each of the plurality of surgical instruments may be placed on a table. In an embodiment, the table may correspond to a surgical table placed in an operation room of a hospital. Examples of the plurality of surgical instruments may include, but is not limited to, scissors, surgical blades, forceps, retractors, tapered needle, clamps, suctions, staplers and clips, orthopedic surgical instruments, and laparoscopic surgical instruments.
[017] Moreover, each of the set of images and the video stream is captured from initiation till completion of a surgical process. In addition, each of the set of images may be iteratively captured at expiry of a pre-defined time interval. Once the set of images and the video stream is captured, at step 304, the at least one of the set of images and the video stream may be processed. The at least one of the set of images and the video stream may be processed in order to create a data log associated with each of the plurality of surgical instruments. In an embodiment, the at least one of the set of images and the video stream may be processed using an Al model. In reference to FIG. 1, the Al model may correspond the Al model 104. In an embodiment, the Al

model may include a deep learning model and a machine learning model. The deep learning model may be configured to analyze and identify each of the plurality of surgical instruments. A method of processing the at least one of the set of images and the video stream has been further explained in detail in conjunction to FIG. 5.
[018] Once the data log is created, at step 306, the created data log may be analyzed. The data log may be analyzed in order to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments. In an embodiment, the at least one usage attribute may be identified based on analysis of the data log performed by the machine learning model of the Al model. In an embodiment, the at least one usage attribute of each of the plurality of surgical instruments may include, but is not limited to, at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need. Upon identifying the at least one usage attribute, at step 308, each of the at least one usage attribute identified for each of the at least one surgical instrument may be compared with associated thresholds.
[019] The thresholds associated with each of the at least one usage attribute may correspond to a pre-defined threshold for each of the at least one usage attribute. It should be noted that, the associated threshold for each of the at least one usage attribute may be user-defined. In an embodiment, each of the at least one usage attribute may be compared with the associated threshold in order to identify one or more of the at least one usage attribute associated with one of the plurality of surgical instrument with higher threshold value than the associated threshold. The identification of the one or more of the at least one usage attribute having higher threshold value than the associated threshold may be done in order to notify at least one user about the one or more of the plurality of surgical instrument with higher threshold value than the associated thresholds. The at least one user may be notified in order to take a suitable action for the one or more of the plurality of surgical instrument with higher threshold value.
[020] By way of an example, suppose based on comparison of the at least one usage attribute for a surgical instrument, a user may identify that the at least one usage attribute may have higher threshold value than the associated threshold. Upon detecting the higher threshold value of the at least one usage attribute associated with the surgical instrument, the user may perform back-end analysis of the at least one usage attribute associated with the surgical instrument using the Al model. Based on

the back-end analysis performed, the user may monitor current working condition of the surgical instrument. For example, based on the monitoring of the current working condition, the user may identify at least one of wear and tear, remaining life and usability, and replacement need for the surgical instrument. In an embodiment, the user may correspond to a person working in a surgical department of the hospital.
[021] Further, at step 310, a database may be updated based on comparing each of the at least one usage attribute with the associated thresholds. In reference to FIG. 1, the database may correspond to the database 116. A process of updating the database has been further explained in detail in conjunction to FIG. 6. Once the database is updated, at step 312, at least one user may be notified. The at least one user may be notified based on comparison of the at least one usage attribute identified for each of the at least one surgical instrument with the associated thresholds. In other words, the at least one user may be notified in order to provide details of the current working condition of each of the at least one surgical instrument to the at least one user.
[022] Referring now to FIG. 4, a flow diagram of a system for automating usage analysis of surgical instruments is illustrated, in accordance with an exemplary embodiment. In reference to FIG. 1, the system used for automating usage analysis of surgical instruments disclosed in the present FIG. 4 may correspond to the system 100. At step 402, a Graphical User Interface (GUI) may be provided to the user in order to enable the user to interact with an electronic device. In reference to FIG. 1, the GUI may correspond to the user interface 114 of the electronic device 102. Byway of an example, the user may be able to see the at least one of the set of images and the video stream captured, the created data log, the at least one usage attribute identified, results of comparison of each of the at least one usage attribute with the associated thresholds, etc., via the GUI. In addition, the user may provide inputs (i.e., an image of a surgical instrument for which the user may need to extract the at least one suggestive attribute) via the GUI.
[023] Further, at step 404, inputs from image capturing device may be received. In an embodiment, the inputs may correspond to the at least one of the set of images and the video stream of each of the plurality of surgical instruments placed on the table. In reference to FIG. 1, the image capturing device may correspond to the camera 106 inbuilt in the electronic device 102 or the camera of one of the external device 124. Once the at least one of the set of images and the video stream is

captured, at step 406, each of the set of images and the video stream may be send to a cloud server database for further processing. In reference to FIG. 1, the cloud server database may correspond to the database 120.
[024] Upon receiving the set of images and the video stream, an Al model may process the at least one of the set of images and the video stream in order to create the data log associated with each of the plurality of surgical instruments. In reference to FIG. 1, the Al model may correspond to the Al model 104. In an embodiment, in order to process the at least one of the set of images and the video stream, the Al model may analyze the at least one of the set of images and the video stream to identify each of the plurality of surgical instruments. Each of the plurality of surgical instruments may be identified based on mapping of each of the set of images with one of the plurality of surgical instrument images stored in the cloud server database.
[025] In order to analyze the at least of the set of images and the video stream to identify each of the plurality of surgical instruments, the Al model may use the deep learning model. The deep learning model used may be trained using deep learning algorithms. Examples of the deep learning algorithms used by the deep learning model to analyze and identify each of the plurality of surgical instruments may include, but is not limited to, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), Recurrent Neural Networks (RNNs), Radial Basis Function Networks (RBFNs), Self-Organizing Maps (SOMs). Upon identifying each of the plurality of surgical instruments, the data log associated with each of the plurality of surgical instruments may be created.
[026] Once the data log is created, at step 408, the Al model may analyze the data log to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments. The at least one usage attribute of each of the plurality of surgical instruments may include, but is not limited to, at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement needs. As depicted in the present FIG. 4, at step 410, the Al model may process the data log created for each of the plurality of surgical instruments based on first usage attribute, i.e., usage pattern. In an embodiment, the data log may be analyzed based on the usage pattern to identify usage pattern of each of the plurality of surgical instruments by a surgeon (i.e., a surgeon responsible for performing the surgical process) for performing a specific surgical process based on

a therapeutic area of the surgeon. Examples of therapeutic area may include, but is not limited to, trauma, joint replacement, and spine surgery.
[027] Further, at step 412 the Al model may process the data log created for each of the plurality of surgical instruments based on second usage attribute, i.e., frequency of use. In an embodiment, the data log may be analyzed based on the frequency of use to identify number of times at least one of the plurality of surgical instruments or combination of two or more of the plurality of surgical instruments is used while performing the specific surgical process by the surgeon. Further, at step 414, the Al model may process the data log created for each of the plurality of surgical instruments based on third usage attribute, i.e., usage duration. In an embodiment, the data log may be analyzed based on the usage duration in order to identify duration for which each of the plurality of surgical instruments is used by the surgeon for performing the specific surgical process. In an embodiment, based on analysis of initial three usage attribute (i.e., the usage pattern, the frequency of use, and the usage duration) for each of the plurality of surgical instruments, the Al model may be able to predict results corresponding to remaining usage attribute (i.e., wear and tear, remaining life and usability, and replacement need) for each of the plurality of surgical instruments.
[028] Once the data log for the usage pattern, the frequency of use, and the usage duration is determined, at step 416, each of the at least one usage attribute may provide predicted information to the user for optimization of each of the plurality of surgical instruments. In other words, based on each of the at least one usage attribute determined for each of the plurality of surgical instruments used by the surgeon to perform the specific surgical process, a plurality of profiles may be created for each of the plurality of surgical instruments, a plurality of surgeons, and a plurality of surgical procedures. Based on the plurality of profiles created, the user may be able to analyze each of the plurality of profiles to extract at least one suggestive attribute associated with each of the plurality of surgical instruments for future reference. The at least one suggestive attribute, may include, but is not limited to, at least one of material, design, supply chain, or manufacturing of each of the plurality of surgical instruments.
[029] Further, in order to analyze the data log to identify the at least one usage attribute, the Al model may use the machine learning model. The machine learning model may be trained based on machine learning algorithms. Examples of machine

learning algorithms used by the machine learning model to identify the at least one usage attribute, may include, but is not limited to, Linear Regression, Logistic Regression, Decision Tree, Support Vector Machine (SVM), Naive Bayes algorithm, K-means clustering, Random Forest algorithm.
[030] Referring now to FIG. 5, a flowchart of a method for creating a data log is illustrated, in accordance with an embodiment. With reference to FIG. 3, in order to process at least one of the set of images and the video stream as mentioned in step 304, at step 502, each of the plurality of surgical instruments may be identified. Moreover, each of the plurality of surgical instruments may be identified based on mapping of each of the set of images with one of a plurality of surgical instrument images. As will be appreciated, each of the plurality of surgical instrument images may initially be stored in a cloud server database while training of an Al model. In reference to FIG. 1, the cloud server database may correspond to the database 120. In addition, the Al model may correspond to the Al model 104. It should be noted that, for ease of explanation processing of each of the set of images is considered. However, one or both of the set of images and the video stream may be processed in order to identify the each of the plurality of surgical instruments.
[031] Once each of the plurality of surgical instruments is identified, at step 504, a type of surgical process may be determined from a plurality of surgical processes. The type of surgical process may be determined based on each of the plurality of surgical instruments identified and a set of relevant data captured. Examples of the plurality of surgical processes may include, but is not limited to, cataract surgery, cesarean surgery, general surgery, abdominal surgery, orthopedic surgery, colon and rectal surgery, and neurosurgery. The set of relevant data may include, but is not limited to, details of a surgeon responsible for performing the surgical process, type of table, operating room lights, patient monitor, electrosurgical generators, respiratory ventilators, or other medical instruments and equipment.
[032] In an embodiment, in order to identify each of the plurality of surgical instruments, the deep learning model may be used. The deep learning model used may be trained using deep learning algorithms for each instrument from the plurality of instruments based on the type of surgical process. Examples of the deep learning algorithms used to train the deep learning model may include, but is not limited to, Convolutional Neural Networks (CNNs), Long Short Term Memory Networks (LSTMs), Recurrent Neural Networks (RNNs), Radial Basis Function Networks (RBFNs), Self-

Organizing Maps (SOMs). The trained deep learning model may enable recognition of each instrument in different surgical scenario based on the type of surgical process without a need of re-training the deep learning model. In order to map at least one of the plurality of surgical instruments to one of the plurality of surgical processes an association rule mining may be used. An advantage of using the association rule mining for mapping may include effective tracking of each instrument for at least one of the plurality of surgical processes.
[033] By way of an example, using the trained deep learning model, once each of the plurality of surgical instruments are identified, then based on the plurality of surgical instruments identified, the trained deep learning model may be able to predict the type of surgical process based on each of the plurality of surgical instruments identified and the set of relevant data captured. Further, at step 506, on-time and off-time of each of the plurality of surgical instruments with respect to the table may be determined. In an embodiment, the table may correspond to the surgical table placed in the operation room of the hospital. Upon determining the on-time and off-time of each of the plurality of surgical instruments, at step 508, a time log for each of the plurality of surgical instruments may be maintained based the determined on-time and off-time.
[034] In other words, in order to maintain the time log, the on-time and off-time of each of the plurality of surgical instruments required by the surgeon for performing a specific surgical process may be determined. In an embodiment, the on-time may refer to a stop time stamp at which an individual surgical instruments or a combination of surgical instruments from the plurality of surgical instruments is placed back on the surgical table after usage. In addition, the off-time may refer to a start time stamp at which the individual surgical instruments or the combination of surgical instruments from the plurality of surgical instruments are picked-up from the surgical table for usage. In some embodiment, in addition to the on-time and off-time, an additional time stamp for each of the plurality of surgical instruments may be determined. The additional time-stamp determined may provide details of the usage duration of each of the plurality of surgical instruments by the surgeon for performing the specific surgical process.
[035] Once the time log is maintained, at step 510, the data log may be created for each of the plurality of surgical instruments identified. The data log may be created based on the time log of each of the plurality of surgical process, the type of surgical

process, and the surgeon responsible for performing the surgical process. Once the data log is created the created data log may be analyzed to identify at least one usage attribute for at least one surgical instrument from the plurality of surgical instruments identified. Further, based on the at least one usage attribute identified, comparison of the at least one usage attribute may be done with the associated thresholds in order to update the database and notify at least one user. In reference to FIG. 1, the database may correspond to the database 116.
[036] Referring now to FIG. 6, a flowchart of a method of updating a database is illustrated, in accordance with an embodiment. With reference to FIG. 3, in order to update the database as mentioned in step 310, at step 602, a plurality of profiles may be created. The plurality of profiles may be created for each of the plurality of surgical instruments, a plurality of surgeons, and a plurality of surgical procedures. Once the plurality of profiles is created, at step 604, each of the plurality of profiles may be analyzed to extract at least one suggestive attribute associated with each of the plurality of surgical instruments. In an embodiment, the at least one suggestive attribute corresponds to at least one of material, design, supply chain, or manufacturing of each of the plurality of surgical instruments.
[037] By way of an example, the user may analyze the plurality of profile to extract the at least one suggestive attribute associated with a surgical instrument from the plurality of surgical instruments. The extracted suggestive attribute may enable the user to know about material, design, supply chain, or manufacturing of the surgical instrument. In other words, based on the at least one suggestive attribute extracted, the user may easily identify material required to build the surgical instrument, design of the surgical instrument, frequency at which the surgical instrument is supplied in the hospital and many other similar factors. Based on the at least one suggestive attribute extracted, information gathered for the surgical instrument may enable the user to know about any wear and tear or replacement need associated with the surgical instrument.
[038] Referring now to FIG. 7, a table depicting an updated database file generated based on at least one usage attribute identified for at least one surgical instrument is represented, in accordance with an exemplary embodiment. The updated database file may represent the plurality of profiles created for each of the plurality of surgical instruments, the plurality of surgeons, and the plurality of surgical procedures. In an embodiment, the updated database file represented may be

generated based on results of analysis of the data log created via an Al model. In reference to FIG. 1, the Al model may correspond to the Al 104.
[039] The results of analysis of the data log may provide information about the at least one usage attribute associated with each of the plurality of surgical instruments to the user. In an embodiment, the at least one usage attribute may include, but is not limited to, at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need. Further, the updated database file may enable the user to extract the at least one suggestive attribute associated with each of the plurality of surgical instruments based on his requirement. The at least one suggestive attribute may include, but is not limited to, at least one of material, design, supply chain, or manufacturing of each of the plurality of surgical instruments. In other words, the updated database file generated may enable synthesis of design and knitting strategies associated with each of the plurality of surgical instruments to the user.
[040] The table representing the updated database file depicted in the present FIG. 7 may include a column "S. No. 702" that may represent serial number associated with each of the plurality of surgical instruments. A column "surgical instrument name 704" may represent name of each of the plurality of surgical instruments. A column "specific surgeon 706" may represent usage of at least one of the plurality of surgical instruments by one of the plurality of surgeons for performing a specific surgical process. A column "type of surgery 708" may represent the type of surgical process determined from the plurality of surgical processes. In an embodiment, the type of surgical process may be determined based on each of the plurality of surgical instruments and the set of relevant data captured. The set of relevant data may include, but is not limited to, details of the surgeon responsible for performing the surgical process, type of table, operating room lights, patient monitor, electrosurgical generators, respiratory ventilators, or other medical instruments and equipment.
[041 ] A column "therapeutic area 710" may represent an area of specialization of medical practice of each of the plurality of surgeon. A column "usage pattern 712" may represent pattern of using at least one of the surgical instruments by at least one of the plurality of surgeons for performing the specific surgical process. A column "usage duration 714" may represent duration (i.e., time -period) of usage of at least one of the plurality of surgical instruments by at least one of the plurality of surgeons for performing the specific surgical process. A column "usage frequency 716" may

represent number of times at least one of the plurality of surgical instruments is used by at least one of the plurality of surgeon for performing the specific surgical process. A column "wear and tear 718" may represent occurrence of any damage in at least one of the plurality of surgical instruments before start and after completion of the specific surgical process.
[042] A column "remaining life and usability 720" may represent remaining life and number of times the at least one of the plurality of surgical instruments may be used in future for performing other surgical processes. A column "replacement need 722" may represent need of replacement of the at least one of the plurality of surgical instruments based on predicted wear and tear and remaining life and usability. In an embodiment, information about wear and tear, remaining life and usability, and replacement needs of each of the plurality of surgical instruments may be predicted by the Al model based on captured information of the usage pattern, the usage duration, and the frequency of use of each of the plurality of surgical instruments.
[043] Referring now to FIG. 8, a normal scenario of tracking usage of a plurality of surgical instruments is illustrated, in accordance with an exemplary embodiment.
[044] In present scenario, initially, a worker (e.g., a nurse) 802 may be responsible to de-assemble a kit including each of the plurality of surgical instruments 804 required for performing a specific surgical process. In order to de-assemble the kit, at 806, the worker 802 may arrange each of the plurality of surgical instruments on a surgical table 808. Once each of the plurality of surgical instruments 804 are arranged on the surgical table 808, the worker 802 may be responsible to monitor each of the plurality of surgical instruments. In order to monitor each of the plurality of surgical instruments, at 810, the worker 802 may determine when each of the plurality of surgical instruments is being picked-up (i.e., the off-time) from the surgical table 808 for usage by a surgeon for performing a specific surgical process. Further, at 812, the worker 802 may determine when each of the plurality of surgical instruments is placed back (i.e., the on-time) by the surgeon on the surgical table 808 after usage.
[045] In present scenario, the monitoring of usage of each of the plurality of surgical instruments 804 may be done by the worker 802 either manually or using an existing Radio Frequency Identification (RFID) tracking technique. The existing RFID tacking technique allows the worker 802 to wirelessly track and count each of the plurality of surgical instruments using an antenna present in the operation room. However, the existing RFID tracking technique may only monitor in-time and out-time

of each the plurality of surgical instruments with respect to the operation room. In other words, the RFID tracking technique may monitor a time at which each of the plurality of surgical instruments enters the operation, and a time at which each of the plurality of surgical instruments leaves the operation room. Using the present scenario disclosed in the FIG. 8, the worker 802 may not to able to identify at least one usage attribute associated with each of the plurality of surgical instruments.
[046] Referring now to FIG. 9, a scenario of automated usage analysis of each of a plurality of surgical instruments is illustrated, in accordance with an exemplary embodiment. Initially, during start of a surgical process, a plurality of surgical instruments 904 may be placed on a table 906 as represented via 902. The table 906 may correspond to a surgical table placed in an operation room of a hospital. Once the plurality of surgical instruments 904 are placed on the table 906, at 908, a camera 910 may be mounted overhead on the table 906 to capture at least one of the set of images and the video stream. In present FIG. 9, the at least one of the set of images and the video stream may correspond to a camera feed 912. In an embodiment, the camera feed 912 may be iteratively captured after expiry of a pre-defined time interval in order to capture the camera feed 912 from initiation till completion of a surgical process. As will be appreciated, multiple cameras may be mounted overhead the table 906 or within the operation room to capture the set of images, the video stream, and the set of relevant data.
[047] Based on the camera feed 912 captured, an Al model may be configured to process each of the set of images and the video stream to create the data log. In order to create the data log, initially, the Al model may be configured to identify each of the plurality of surgical instruments 904 based on the captured camera feed 912. In reference to FIG. 1, the Al model may correspond to the Al model 104. In order to identify each of the plurality of surgical instruments, the Al model may map each of the set of images with one of the plurality of surgical instrument images. As will be appreciated, each of the plurality of surgical instrument images may be initially stored in the cloud server database during training of the Al model. In reference to FIG. 1, the cloud server database may correspond to the database 120 of the server 118.
[048] Once identified, the Al model may be configured to determine the type of surgical process based on the plurality of surgical instruments 904 identified and the set of relevant data captured via the camera 910. Upon determining the type of surgical process, at 914, the Al model may determine the off-time of each of the

plurality of surgical instruments 904 from the table 906 using the camera 910. The off time determined for each of the plurality of surgical instruments 904 may be a time at which each of the plurality of surgical instruments 904 is picked-up and removed from the table 906 to perform the surgical process.
[049] Further, at 916, the Al model may determine the on-time of each of the plurality of surgical instruments 904 with respect to the table 906 using the camera 910. The on-time of each of the plurality of surgical instruments 904 may be a time at which each of the plurality of surgical instruments is returned and placed back of the table 906. In an embodiment, the Al model may continuously detect the on-time and the off-time of each of the plurality of surgical instruments 904 from start of the surgical process, during the surgical process until completion of the surgical process. Upon determining the on-time and the off-time of each of the plurality of surgical instruments 904, the Al model may maintain a time log for each of the plurality of surgical instruments 904. The time log for each of the plurality of surgical instruments 904 may be maintained based on the associated on-time and the associated off-time.
[050] Once the surgical process is completed, at 918, the Al model may detect whether each of the plurality of surgical instruments 904 has returned and placed back on the table 906 after usage. In order to detect whether each of the plurality of surgical instruments 904 has returned on the table 906, the Al model may use a camera feed 920 captured using the camera 910. Once each of the plurality of surgical instruments 904 are detected to present on the table 906 and the surgical process is predicted to be complete, the Al model may create the data log for each of the plurality of surgical instruments 904. The data log for each of the plurality of surgical instruments may be created based on the type of surgical process, the type of surgical instruments, and the time log.
[051] Once the data log is created, at 922, the created data log may be analyzed at back-end in order to identify the at least usage attribute associated with at least one surgical instrument from the plurality of surgical instruments 904. The at least one usage attribute may include, but is not limited to at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need. Further, each of the at least one usage attribute identified for each of the at least one surgical instrument may be compared with the associated thresholds. As will be appreciated, the thresholds associated with each of the at least one usage attribute may be pre-defined by the user. Based on comparison of each of

the at least one usage attribute with the associated thresholds, the database may be updated. In reference to FIG. 1 the database may correspond to the database 116. Further, in addition to updating the database, the at least one user may be notified to perform the suitable corrective action. In an embodiment, the database updated may enable the plurality of users to extract at least one suggestive attribute associated with each of the plurality of surgical instruments based on his requirement. The at least one suggestive attribute may include, but is not limited to, at least one of material, design, supply chain, or manufacturing of each of the plurality of surgical instruments.
[052] By way of an example, suppose the threshold associated with third usage attribute (i.e., the frequency of use) for one of at least one surgical instrument (e.g., scissor) may be determined to be above than the associated threshold pre-defined for the third usage attribute. Then, the user may perform a check for the scissor to determine any wear and tear, remaining life and usability, and replacement need associated with the scissor. Based on the check performed and the associated threshold determined, the user may perform the suitable corrective action. In present example, the suitable corrective action may correspond to replacement of the scissor with a new scissor.
[053] Various embodiments provide method and system for automating usage analysis of surgical instruments. The disclosed method and system may capture at least one of a set of images and a video stream of a plurality of surgical instruments placed on a table. Each of the set of images and the video stream is captured from initiation till completion of a surgical process. Moreover, the disclosed method and system may process at least one of the set of images and the video stream. The set of images and the video stream are processed via the Al model to create a data log associated with each of the plurality of surgical instruments. Further, the disclosed method and system may analyze the data log to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments. In addition, the disclosed method and system may compare, for each of the at least one surgical instrument, each of the at least one usage attribute with associated thresholds. Further, the disclosed method and system may update a database based on comparing each of the at least one usage attribute with the associated thresholds. Thereafter, the disclosed method and system may notify at least one user in response to the comparing.

[054] The system and method disclose may provide some advantages like, the system and the method may be implemented directly, without need for change or modification in existing environment. For implementation of the system and method no added cost may be required for incorporating any type of marker, such as visual means or by electronic means. Further, the disclosed system and method may understand each surgical instrument even in random layout (e.g., surgical instruments arranged on a surgical table), hence, there is no change required in existing surgical practices to implement the disclosed system. Moreover, the disclosed system and method may use deep learning algorithms that learns over a period of time about usage of surgical instruments thereby improving accuracy of the disclosed system. Further, the disclosed system and method may analyze information associated with frequency of use of the surgical instruments in order to provide valuable inputs to manufacturer about condition of the surgical instruments, i.e., performance and regulatory point of view associated with each surgical instrument to restrict usage of the surgical instruments after a certain number of lifecycles.
[055] In addition, the disclosed system and method may provide information about usage duration and usage pattern of each of the surgical instruments. Further, the information captured about usage duration and usage pattern of each of the surgical instruments may be valuable for manufacturer in order to optimize and improve design of each of the surgical instruments. For example, surgical instruments that may not be used for long duration with adequate robustness may be defined by design. In addition, the usage pattern may help the manufactures to drive design and kitting strategies for next-generation of the surgical instruments. Moreover, the disclosed system and method may reduce need for human intervention inside the OR for collecting the information associated with each of the surgical instruments. For example, the disclosed system and method may reduce need of a sales representative required for observing surgical process.
[056] Additionally, the disclosed system and method may also work with any commodity camera thereby eliminating need of specialized developed equipment and tagging devices. Moreover, the disclosed system and method may even utilize feed from any exiting aerial mounted cameras present in the operation room of the hospital. Further, the disclosed system and method may utilize analytics engine that runs from cloud, thereby eliminating need of any high-end computational devices inside the operation room to perform analysis of the surgical instruments. Moreover, since, the

disclosed system and method may use deep learning algorithms for image analysis, therefore any external factors affecting picture quality such as lighting, shadows, camera resolution, etc. may have minimal impact on outcome of analysis of the surgical instruments.
[057] It will be appreciated that, for clarity purposes, the above description has described embodiments of the invention with reference to different functional units and processors. However, it will be apparent that any suitable distribution of functionality between different functional units, processors or domains may be used without detracting from the invention. For example, functionality illustrated to be performed by separate processors or controllers may be performed by the same processor or controller. Hence, references to specific functional units are only to be seen as references to suitable means for providing the described functionality, rather than indicative of a strict logical or physical structure or organization.
[058] Although the present invention has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Rather, the scope of the present invention is limited only by the claims. Additionally, although a feature may appear to be described in connection with particular embodiments, one skilled in the art would recognize that various features of the described embodiments may be combined in accordance with the invention.
[059] Furthermore, although individually listed, a plurality of means, elements or process steps may be implemented by, for example, a single unit or processor. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and the inclusion in different claims does not imply that a combination of features is not feasible and/or advantageous. Also, the inclusion of a feature in one category of claims does not imply a limitation to this category, but rather the feature may be equally applicable to other claim categories, as appropriate.

CLAIMS
WHAT IS CLAIMED IS:
1. A method for automating usage analysis of surgical instruments, the method
comprising:
capturing (302) at least one of a set of images and a video stream of a plurality of surgical instruments placed on a table, wherein each of the set of images and the video stream is captured from initiation till completion of a surgical process;
processing (304), by an Artificial Intelligence (Al) model (104), at least one of the set of images and the video stream, wherein the set of images and the video stream are processed via the Al model to create a data log associated with each of the plurality of surgical instruments;
analyzing (306), by the Al model (104), the data log to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments;
comparing (308), for each of the at least one surgical instrument, each of the at least one usage attribute with associated thresholds;
updating (310) a database (116) based on comparing each of the at least one usage attribute with the associated thresholds; and
notifying (312) at least one user in response to the comparing.
2. The method of claim 1, wherein each of the set of images is iteratively captured at expiry of a pre-defined time interval.
3. The method of claim 1, wherein processing (304) of the at least one of the set of images and the video stream to create the data log comprises:
identifying (502), by the Al model (104), each of the plurality of surgical instruments, wherein each of the plurality of surgical instruments is identified based on mapping of each of the set of images with one of a plurality of surgical instrument images; and
determining (504), by the Al model (104), a type of surgical process from a plurality of surgical processes based on each of the plurality of surgical instruments identified and a set of relevant data captured, wherein the set of relevant data includes details of a surgeon responsible for performing the surgical process, type of table,

operating room lights, patient monitor, electrosurgical generators, respiratory ventilators, or other medical instruments and equipment;
determining (506), by the Al model (104), an on-time and an off-time of each of the plurality of surgical instruments with respect to the table;
maintaining (508), by the Al model (104), a time log for each of the plurality of surgical instruments, based on associated on-time and off-time; and
creating (510), by the Al model (104), the data log for each of the plurality of surgical instruments identified, based on the type of surgical process, the type of surgical instruments, and the time log.
4. The method of claim 1, wherein:
the at least one usage attribute of each of the plurality of surgical instruments comprises at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need; and
the Al model (104) includes a deep learning model and a machine learning model, and wherein the deep learning model is configured to analyze and identify each of the plurality of surgical instruments, and wherein the machine learning model is configured to analyze the data log to identify the at least one usage attribute.
5. The method of claim 1, wherein updating (310) the database (116) comprises:
creating (602) a plurality of profiles for each of the plurality of surgical instruments, a plurality of surgeons, and a plurality of surgical procedures; and
analyzing (604) each of the plurality of profiles to extract at least one suggestive attribute associated with each of the plurality of surgical instruments, wherein the at least one suggestive attribute corresponds to at least one of material, design, supply chain, or manufacturing of each of the plurality of surgical instruments.
6. A system (100) for automating usage analysis of surgical instruments, the system
(100) comprising:
a processor (110); and
a memory (108) communicatively coupled to the processor (110), wherein the memory (108) stores processor executable instructions, which, on execution, causes the processor (110) to:

capture (302) at least one of a set of images and a video stream of a plurality of surgical instruments placed on a table, wherein each of the set of images and the video stream is captured from initiation till completion of a surgical process;
process (304) at least one of the set of images and the video stream, wherein the set of images and the video stream are processed via the Al model to create a data log associated with each of the plurality of surgical instruments;
analyze (306) the data log to identify at least one usage attribute associated with at least one surgical instrument from the plurality of surgical instruments;
compare (308), for each of the at least one surgical instrument, each of the at least one usage attribute with associated thresholds;
update (310) a database based on comparing each of the at least one usage attribute with the associated thresholds; and
notify (312) at least one user in response to the comparing.
7. The system (100) of claim 6, wherein each of the set of images is iteratively captured at expiry of a pre-defined time interval.
8. The system (100) of claim 6, wherein, to process (304) the at least one of the set of images and the video stream to create the data log, the processor executable instructions cause the processor (110) to:
identify (502) each of the plurality of surgical instruments, wherein each of the plurality of surgical instruments is identified based on mapping of each of the set of images with one of a plurality of surgical instrument images; and
determine (504) a type of surgical process from a plurality of surgical processes based on each of the plurality of surgical instruments identified and a set of relevant data captured, wherein the set of relevant data includes details of a surgeon responsible for performing the surgical process, type of table, operating room lights, patient monitor, electrosurgical generators, respiratory ventilators, or other medical instruments and equipment;
determine (506) an on-time and an off-time of each of the plurality of surgical instruments with respect to the table;

maintain (508) a time log for each of the plurality of surgical instruments, based on associated on-time and off-time; and
create (510) the data log for each of the plurality of surgical instruments identified, based on the type of surgical process, the type of surgical instruments, and the time log.
9. The system (100) of claim 6, wherein:
the at least one usage attribute of each of the plurality of surgical instruments comprises at least one of usage pattern, usage duration, a frequency of use, wear and tear, remaining life and usability, or replacement need; and
the Al model (104) includes a deep learning model and a machine learning model, and wherein the deep learning model is configured to analyze and identify each of the plurality of surgical instruments, and wherein the machine learning model is configured to analyze the data log to identify the at least one usage attribute.
10. The system (100) of claim 6, wherein, to update (310) the database, the processor
executable instructions cause the processor (110) to:
create (602) a plurality of profiles for each of the plurality of surgical instruments, a plurality of surgeons, and a plurality of surgical procedures; and
analyze (604) each of the plurality of profiles to extract at least one suggestive attribute associated with each of the plurality of surgical instruments, wherein the at least one suggestive attribute corresponds to at least one of material, design, supply chain, or manufacturing of each of the plurality of surgical instruments.