Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
RECOMMENDING A GUIDANCE FOR ULTRASOUD DEVICE SETTINGS

Applicant:
Qure.ai Technologies Private Limited
An Indian company having address as:
Level 7, Commerz II, International Business Park, Oberoi Garden City, Off Western Express Highway, Goregaon (East), Mumbai 400063, Maharashtra, India

The following specification describes the invention and the manner in which it is to be performed.
PRIORITY INFORMATION
[001] The present application does not claim a priority from any other application.
TECHNICAL FIELD
[002] The present subject matter described herein, in general, relates to a system and a method for recommending a guidance for ultrasound device settings. More particularly, to recommending the guidance for the ultrasound device settings.
BACKGROUND
[003] Currently, ultrasound scans are very operator dependent. This is because the ultrasound scans are acquired by radiologists manually. Further, the radiologists prepare reports for a patient based on the acquired images. It must be noted that the same patient may get different reports, and hence have a different diagnosis, when the patient goes to different radiologists in same hospital, different radiologists in different hospitals or same radiologist in same or different hospital after a few days. This happens because unlike other modalities such as X-rays, Computed Tomography (CT) scans, a scanning device needs to be manually moved for an ultrasound procedure while keeping correct device settings. If the device settings are not correct, it may lead to an inaccurate measurement of blood velocity. This may further lead to misdiagnosis.
SUMMARY
[004] Before the present system(s) and method(s), are described, it is to be understood that this application is not limited to the particular system(s), and methodologies described, as there can be multiple possible embodiments which are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular implementations or versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system and a method for recommending a guidance for ultrasound image settings. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[005] In one implementation, a method for recommending a guidance for ultrasound image settings is disclosed. Initially, an ultrasound image in real-time may be received. Further, a region of interest of the ultrasound image may be detected based on an analysis of the ultrasound image using deep learning model. Furthermore, vessel boundaries in the region of interest may be identified. In one aspect, the vessel boundaries may be identified based on an analysis of the region of interest of the ultrasound image using the deep learning model. The vessel boundaries may be identified based on vessel segmentation. Subsequently, doppler structures and doppler parameters may be identified using a machine learning technique upon detection of the vessel boundaries. In one aspect, the doppler structures may comprise a color box, a doppler beam line, a doppler angle line and gate. The doppler parameters may comprise a steering angle, a doppler angle, a gate position and a gate size. Further, a blood flow direction may be determined based on the vessel boundaries, the doppler structures and the doppler parameters. Furthermore, an optimization in the doppler structures and the doppler parameters may be determined based on the blood flow direction, predefined doppler structures and predefined doppler parameters. In one aspect, the optimization may include a change in the steering angle, a change in the doppler angle, a change in the gate position, and a change in the gate size. Finally, a guidance for an ultrasound device setting may be recommended based on the optimization. In one aspect, the recommendation may be visual recommendation or audio recommendation. In one aspect, the aforementioned method for recommending a guidance for ultrasound image settings may be performed by a processor using programmed instructions stored in a memory.
[006] In another implementation, a non-transitory computer readable medium embodying a program executable in a computing device for recommending a guidance for ultrasound image settings is disclosed. The program may comprise a program code for receiving an ultrasound image in real-time. Further, the program may comprise a program code for detecting a region of interest of the ultrasound image based on an analysis of the ultrasound image using deep learning model. Furthermore, the program may comprise a program code for identifying vessel boundaries in the region of interest. In one aspect, the vessel boundaries may be identified based on an analysis of the region of interest of the ultrasound image using the deep learning model. The vessel boundaries may be identified based on vessel segmentation. Subsequently, the program may comprise a program code for identifying doppler structures and doppler parameters using a machine learning technique upon detection of the vessel boundaries. In one aspect, the doppler structures may comprise a color box, a doppler beam line, a doppler angle line and gate. The doppler parameters may comprise a steering angle, a doppler angle, a gate position and a gate size. Further, the program may comprise a program code for determining a blood flow direction based on the vessel boundaries, the doppler structures and the doppler parameters. Furthermore, the program may comprise a program code for determining an optimization in the doppler structures and the doppler parameters based on the blood flow direction, predefined doppler structures and predefined doppler parameters. In one aspect, the optimization may include a change in the steering angle, a change in the gate position, a change in the doppler angle, and a change in the gate size. Finally, the program may comprise a program code for recommending a guidance for an ultrasound device setting based on the optimization. In one aspect, the recommendation may be visual recommendation or audio recommendation.
BRIEF DESCRIPTION OF THE DRAWINGS
[007] The foregoing detailed description of embodiments is better understood when read in conjunction with the appended drawings. For the purpose of illustrating of the present subject matter, an example of construction of the present subject matter is provided as figures, however, the invention is not limited to the specific method and system for recommending a guidance for ultrasound image settings disclosed in the document and the figures.
[008] The present subject matter is described in detail with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer various features of the present subject matter.
[009] Figure 1 illustrates a network implementation of a system for recommending a guidance for ultrasound image settings, in accordance with an embodiment of the present subject matter.
[010] Figure 2 shows a doppler structures, in accordance with an embodiment of the present subject matter.
[011] Figure 3A shows recommendation for an optimization of a steering angle, in accordance with an embodiment of the present subject matter.
[012] Figure 3B shows recommendation for an optimization of a doppler angle, in accordance with an embodiment of the present subject matter.
[013] Figure 3C shows recommendation for an optimization of a gate position, in accordance with an embodiment of the present subject matter.
[014] Figure 3D shows recommendation for an optimization of a gate size, in accordance with an embodiment of the present subject matter.
[015] Figure 4 illustrates a block diagram of the system for recommending a guidance for ultrasound image settings, in accordance with an embodiment of the present subject matter.
[016] Figure 5 illustrates a method for recommending a guidance for ultrasound image settings, in accordance with an embodiment of the present subject matter.
[017] Figure 6 illustrates an exemplary embodiment of the system, in accordance with an embodiment of the present subject matter.
[018] The figures depict an embodiment of the present disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION
[019] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words “receiving”, "detecting," "identifying,” “determining," "recommending," and other forms thereof, are intended to be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any system and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, system and methods are now described.
[020] The disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments described, but is to be accorded the widest scope consistent with the principles and features described herein.
[021] The present subject matter discloses a system and a method for recommending a guidance for ultrasound image settings. Typically, ultrasound scans are operator dependent. Most of the times, same patient may get different images or reports, and hence have a different diagnosis if they go to different radiologists in same hospital, different radiologists in different hospitals or same radiologist in same or different hospital after a few days. Thus, the conventional process is time consuming and unreliable. More importantly, the present invention discloses a cost effective, reliable and an automatic process for recommending a guidance for ultrasound image settings. Initially, an ultrasound image may be received. Further, doppler structures and doppler parameters may be identified. Finally, a guidance for optimization of the doppler structures and the doppler parameters may be recommended.
[022] While aspects of described system and method for recommending a guidance for ultrasound image settings may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
[023] Referring now to Figure 1, a network implementation 100 of a system 102 for recommending a guidance for ultrasound image settings is disclosed. It may be noted that one or more users may access the system 102 through one or more user devices 104-1, 104-2…104-N, collectively referred to as user devices 104, hereinafter, or applications residing on the user devices 104. The user may also connect the wireless display device to a handheld ultrasound device 114. The handheld ultrasound device 114 may be connected to the network 106 and the user device 104. In one aspect, the one or more users may comprise a radiologist, a doctor, a lab assistance and the like.
[024] Although the present disclosure is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a virtual environment, a mainframe computer, a server, a network server, a cloud-based computing environment. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N. In one implementation, the system 102 may comprise the cloud-based computing environment in which the user may operate individual computing systems configured to execute remotely located applications. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[025] In one implementation, the network 106 may be a wireless network, a wired network, or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[026] In one embodiment, the system 102 may include at least one processor 108, an input/output (I/O) interface 110, and a memory 112. The at least one processor 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, Central Processing Units (CPUs), state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 108 is configured to fetch and execute computer-readable instructions stored in the memory 112.
[027] The I/O interface 110 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 110 may allow the system 102 to interact with the user directly or through the client devices 104. Further, the I/O interface 110 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 110 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 110 may include one or more ports for connecting a number of devices to one another or to another server.
[028] The memory 112 may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or nonvolatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, Solid State Disks (SSD), optical disks, and magnetic tapes. The memory 112 may include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The memory 112 may include programs or coded instructions that supplement applications and functions of the system 102. In one embodiment, the memory 112, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the programs or the coded instructions.
[029] As there are various challenges observed in the existing art, the challenges necessitate the need to build the system 102 for recommending a guidance for ultrasound device settings. At first, a user may use the user device 104 to access the system 102 via the I/O interface 110. The user may register the user devices 104 using the I/O interface 110 in order to use the system 102. In one aspect, the user may access the I/O interface 110 of the system 102. The detail functioning of the system 102 is described below with the help of figures.
[030] The present subject matter describes the system 102 for recommending a guidance for ultrasound image settings. The guidance may be recommended in real-time to the user for acquiring the 3D ultrasound image using the handheld ultrasound device 114. The guidance may include an audio guidance, a text guidance and the like. In order to recommend the guidance, initially, the system 102 may receive an ultrasound image of a patient. The ultrasound image may be received in real-time. Once received, the ultrasound image may be displayed to the user. In one aspect, an ultrasound video may be received.
[031] Further, the system 102 may analyze the ultrasound image. Based on the analysis, the system 102 may detect a region of interest. In one aspect, the ultrasound image may be analyzed using deep learning technique. The system 102 may compare the ultrasound image with historical data. Based on the comparison, the region of interest may be detected. The historical data may comprise historical ultrasound images, historical region of interest associated with the historical ultrasound images and the like.
[032] Subsequently, the system 102 may identify vessel boundaries in the region of interest. The vessel boundaries may be identified based on an analysis of the region of interest of the ultrasound image. The vessel boundaries may be identified using the deep learning model. The vessel boundaries may be identified based on vessel segmentation. In one aspect, the deep learning model may be configured to segment vessels in the ultrasound image of the patient. Further, the deep learning model may mark boundaries of the vessels present in ultrasound image.
[033] Once the vessel boundaries are identified, the system 102 may identify doppler structures and doppler parameters using a machine learning technique. The doppler structures may comprise a color box, a doppler beam line, a doppler angle line and gates. The doppler parameters may comprise a steering angle, a doppler angle, a gate position and a gate size. The doppler structures and the doppler parameters may be identified using machine learning technique. In one aspect, the system 102 may analyze historical doppler parameters and historical doppler structures. Based on the analysis, the system 102 may identify the doppler structures and the doppler parameters. Further, the doppler structures may be shown in figure 2.
[034] Referring now to figure 2, a doppler structure is shown, in accordance with an embodiment of the present subject matter. In one embodiment, the color box 202 may be a parallelogram in shape. Further, the doppler beam line 208 may be parallel to the color box 202. Furthermore, the gates 206 may indicate a sample volume in the vessel boundary for which measurement is performed.
[035] In one aspect, the steering angle may be an angle between a vertical and a side of the color box 202. The doppler angle may be defined as angle between the angle line 204 and the beam line 208. It may be understood that the doppler angle is further dependent on angle between a blood flow direction 210 and the doppler angle line 204, and angle between the doppler angle line 204 and the doppler beam line 208. Further, the gate position may be based on an intersection point for the vessel boundaries and the doppler beam line 208. The gate position may be dependent on distance between the gates and the intersection point. Furthermore, the gate size may be a distance between the gates.
[036] The system 102 may determine the blood flow direction 210. The blood flow direction 210 may be determined based on the vessel boundaries, the doppler structures and the doppler parameters. In one aspect, the blood flow direction 210 may be based on the doppler beam line 208 and the vessel boundaries.
[037] Upon determining the blood flow direction 210, the system 102 may determine an optimization in the doppler structures and the doppler parameters. The optimization in the doppler structures and the doppler parameters may be based on the blood flow direction 210, predefined doppler structures and predefined doppler parameters. The optimization may include a change in the steering angle, a change in position of the color box 202, a change in gate position, a change in doppler angle, and a change in gate size. In one aspect, a line related to the blood flow direction 210 may be referred as a flow line.
[038] Subsequently, the system 102 may recommend a guidance for an ultrasound device setting based on the optimization. The guidance may be recommended in order to change at least one of the steering angle, the doppler angle, the gate position or the gate size. The recommendation may a visual recommendation or an audio recommendation. In one aspect, the guidance may be notified to the user.
[039] In one embodiment, the guidance may be recommended using an Artificial Intelligence (AI) technique. The system 102 may analyze historical guidance stored in a database. Based on the analysis of the historical guidance, the system 102 may recommend the guidance to the user. Based on the guidance, the user may change the ultrasound device settings. In one implementation, the ultrasound device may have an ability to auto-optimize the device setting based on the AI guidance. In such case of auto-optimization, the user may not need to make the changes in the ultrasound device settings manually. Once the ultrasound device settings are changed, either automatically or manually, the system 102 may generate a report for the patient. The user may be a doctor, a radiologist and the like. It may be understood that the AI technique is device agnostic. Therefore, the AI technique may be customized to work with the ultrasound images received from any ultrasound device.
[040] In one embodiment, the recommendation for the optimization may be explained further with figure 3A, figure 3B, figure 3C and figure 3D.
[041] Referring now to figure 3A, recommendation for an optimization in the steering angle is shown, in accordance with an embodiment of the present subject matter. In one embodiment, the angle between the blood flow direction and the doppler beam line should be less than 60°. Further, the angle greater than 60° may lead to an error in a velocity measurement. The doppler beam line may be parallel to edges of a side of the color box, hence an orientation of the doppler beam line can be controlled by changing the steering angle. In one aspect, if the blood flow direction is horizontal 301, then the steering angle may be on a left side or a right side of the doppler beam line. In another aspect, if the blood flow direction is tilted upward 303, then the steering angle may be on the left side of the doppler beam line. In another aspect, if the blood flow direction is tilted downward 305, the steering angle may be on the right side of the doppler beam line.
[042] In one embodiment, the system 102 may measure the angle between the blood flow direction and the doppler beam line. In one example, the system 102 may measure the angle between the blood flow direction and the edges of the side of the color box side when the doppler beam line is not visible. Further, the system 102 may adjust the steering angle so that the measured angle is less than or equal to 60°. Based on the angle, the system 102 may recommend the guidance for the steering angle as the left angle or the right angle.
[043] Referring now to figure 3B, a recommendation for an optimization in the doppler angle is shown, in accordance with an embodiment of the present subject matter. In one embodiment, the doppler angle 310 may be an angle between the doppler angle line 312 and the doppler beam line 308. The system 102 may align the doppler angle lines as close to the blood flow direction. The doppler angle may not exceed 60°.
[044] In one embodiment, the system 102 may measure a first angle between the blood flow direction and the doppler angle line 312. Further, the system 102 may measure a second angle between the doppler angle line 312 and the doppler beam line 308. Subsequently, the system 102 may adjust the doppler angle line 312 so that the first angle is as small as possible given the constraint that the second angle is not greater than 60°. Finally, the system 102 may recommend rotation direction i.e., clockwise or anti-clockwise, and magnitude, in degrees based on the angle A1 and the angle A2.
[045] Referring now to figure 3C, recommendation for an optimization of the gate position is shown, in accordance with an embodiment of the present subject matter. In one embodiment, the gates 314 should be centrally located in the blood vessel. The system 102 may get a set of intersection points for the vessel boundaries and the doppler beam line. Further, the system 102 may measure a distance between the gates 314 and each intersection point. The system 102 may further adjust a position of the gates 314 to make the distance between the gates 314 and each intersection point as equal. Finally, the system 102 may recommend the guidance for movement of the gates in upward or downward direction based on the distance.
[046] Referring now to figure 3D, recommendation for an optimization of the gate size is shown, in accordance with an embodiment of the present subject matter. In one embodiment, the gate size may be within a predefined threshold for a blood vessel to be scanned. It may be understood that the predefined threshold for the blood vessel may be between 1mm to 3 mm. It may be understood that the gate size is variable and is adjusted as per artery size. For instance, for the carotid artery the predefined threshold of the gate size may be between 1 to 3mm, and for the vertebral artery the pre-defined threshold of the gate size may measure 1 to 1.5mm. It may be noted that the predefined threshold for the gate size is <=50% of the vessel diameter size. The system 102 may measure a gate distance between 2 gates lines 316. Further, the system 102 may adjust a size of the gates to make the gate size in range of 1mm to 3mm. In one aspect, the gate distance may be in the range of 1mm to 3mm. The system 102 may recommend the guidance to adjust the distance between the 2 gate lines.
[047] In one embodiment, the ultrasound images may be extracted from ultrasound video loops using a heuristic-based method. The heuristic-based method may use visual cues from an individual frame, and changes in an intensity, a color and structure across consecutive frames to detect different regions of video such as B-mode, Color Doppler PW Doppler as well as longitudinal v/s transverse. The heuristic-based method may aim at reducing human effort for image extraction from ultrasound videos. The extracted data may be then labelled by the radiologist for training of various models.
[048] In one embodiment, the system 102 may comprise optimal measurement of a blood flow velocity based on the recommendation. The blood flow velocity may be referred as a doppler velocity. The system 102 may further prevent misdiagnosis by preventing an underestimation of the doppler velocity and a overestimation of the doppler velocity.
[049] Referring now to figure 4, a block diagram of the system for recommending a guidance for ultrasound image settings is illustrated, in accordance with an embodiment of the present subject matter.
[050] At block 402, an input frame may be received. The input frame may correspond to an ultrasound image of a patient.
[051] At block 404, a region detection module may analyze the ultrasound image. Based on the analysis, the region detection module may detect a region of interest of the ultrasound image at block 406. The region of interest may be detected using a deep learning model.
[052] At block 410, a vessel segmentation model may segment vessels in the ultrasound image. Further, the vessel segmentation model may mark boundaries of the vessels upon segmentation. Furthermore, the vessel boundaries are identified at block 412.
[053] At block 408, a doppler detection module may analyze the region of interest. Based on the analysis, doppler structures such as a color box, a beam line, an angle and a gate size may be determined at block 414.
[054] At block 416, the doppler structures and the vessel boundaries may be analyzed using the deep learning model. Upon analysis, a blood flow direction may be detected at block 418.
[055] At block 420, a doppler guidance may be provided based on the blood flow direction and the doppler structures. Further, at block 422, a final guidance may be recommended to a user. The final guidance may be displayed on a screen of the system 102 or an audio may be used for the final guidance.
[056] Referring now to Figure 5, a method 500 for recommending a guidance for ultrasound device settings is shown, in accordance with an embodiment of the present subject matter. The method 500 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types.
[057] The order in which the method 500 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 500 or alternate methods for recommending a guidance for ultrasound device settings. Additionally, individual blocks may be deleted from the method 500 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 500 for recommending the guidance for the ultrasound image settings can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 500 may be considered to be implemented in the above described system 102.
[058] At block 502, an ultrasound image may be received in real-time. In one example, a set of ultrasound images may be received.
[059] At block 504, a region of interest of the ultrasound image may be detected based on an analysis of the ultrasound image using deep learning model.
[060] At block 506, vessel boundaries in the region of interest may be identified. In one aspect, the vessel boundaries may be identified based on an analysis of the region of interest of the ultrasound image using the deep learning model. The vessel boundaries may be identified based on vessel segmentation.
[061] At block 508, doppler structures and doppler parameters may be determined using a machine learning technique upon detection of the vessel boundaries. In one aspect, the doppler structures may comprise a color box, a doppler beam line, a doppler angle line and gate. The doppler parameters may comprise a steering angle, a doppler angle, a gate position and a gate size.
[062] At block 510, a blood flow direction may be determined based on the vessel boundaries, the doppler structures and the doppler parameters.
[063] At block 512, an optimization in the doppler structures and the doppler parameters may be determined based on the blood flow direction, predefined doppler structures and predefined doppler parameters. In one aspect, the optimization may include a change in steering angle, a change in position of the color box, a change in gate position, a change in doppler angle, and a change in gate size.
[064] At block 514, a guidance for an ultrasound device setting may be recommended based on the optimization. In one aspect, the recommendation may be a visual recommendation or an audio recommendation.
[065] Referring now to Figure 6, an exemplary embodiment 600 of the system 102 recommending a guidance for an ultrasound device setting is illustrated. It may be understood that the guidance for the ultrasound device setting is based on the optimization. Further, the recommendation is a visual recommendation. The system 102 is providing the guidance 601 by displaying “1-3 mm is ideal for CCA (Carotid artery)” for the gate size, “Central lumen is ideal for velocity readings” for the gate position, and “Angle parallel to arterial walls is ideal” for the gate angle respectively.
[066] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[067] Some embodiments of the system and the method allows a user to accept or reject a guidance suggestion.
[068] Some embodiments of the system and the method enable an auto-mode, where changes in doppler parameters can be made without end-user feedback to accept or reject.
[069] Some embodiments of the system and method enable visual cues and audio cues for recommendation of a guidance.
[070] Some embodiments of the system and method reduces a turnaround time for capturing a diagnostic quality ultrasound scan including images and final report with accurate stenosis grading.
[071] Some embodiments of the system and method estimates at least 20-30% time saving through an Artificial Intelligence (AI)-based guidance.
[072] Some embodiments of the system and method estimate at least 20-70% improvement in accuracy of the doppler velocities.
[073] Although implementations for methods and system for recommending a guidance for ultrasound device settings have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for recommending the guidance for the ultrasound image settings.

, Claims:
1. A method (500) to recommend a guidance for ultrasound device settings, wherein the method comprises:
receiving, by a processor (108), an ultrasound image in real-time;
detecting, by the processor (108), a region of interest of the ultrasound image based on an analysis of the ultrasound image using a deep learning model;
identifying, by the processor (108), vessel boundaries in the region of interest, wherein the vessel boundaries are identified based on an analysis of the region of interest of the ultrasound image using the deep learning model, wherein the vessel boundaries are identified based on vessel segmentation;
identifying, by the processor (108), doppler structures and doppler parameters using a machine learning technique upon detection of the vessel boundaries, wherein the doppler structures comprise a color box, a doppler beam line, a doppler angle line and gate; and wherein the doppler parameters comprises a steering angle, a doppler angle, a gate position and a gate size;
determining, by the processor (108), a blood flow direction based on the vessel boundaries, the doppler structures and the doppler parameters;
determining, by the processor (108), an optimization in the doppler structures and the doppler parameters based on the blood flow direction, predefined doppler structures and predefined doppler parameters, wherein the optimization includes a change in steering angle, a change in position of the color box, a change in gate position, a change in doppler angle, and a change in gate size; and
recommending, by the processor (108), a guidance for an ultrasound device setting based on the optimization, wherein the recommendation is a visual recommendation or an audio recommendation.
2. The method (500) as claimed in claim 1, comprises optimal measurement of a blood flow velocity based on the recommendation.
3. The method (500) as claimed in claim 1, wherein the guidance is recommended using an Artificial Intelligence (AI) technique.
4. The method (500) as claimed in claim 1, wherein the change in steering angle comprises:
measuring, by the processor, an angle between the blood flow direction (210) and the doppler beam line (208); and
adjusting, by the processor, the steering angle based on the angle.
5. The method (500) as claimed in claim 1, wherein the change in doppler angle comprises:
measuring, by the processor, a first angle between the blood flow direction and the doppler angle line;
measuring, by the processor, a second angle between the doppler angle line and the doppler beam line; and
aligning, by the processor, the doppler angle line to make the first angle minimum, and the second angle less than 60 degree.
6. The method (500) as claimed in claim 1, wherein the change in the gate position comprises:
determining, by the processor, a set of intersection points for the vessel boundaries and the doppler beam line;
measuring, by the processor, a distance between the gates and each intersection point; and
aligning, by the processor, gate position based on the distance.
7. The method (500) as claimed in claim 1, wherein the change in the gate size comprises:
measuring, by the processor, a gate distance between two lines of the gates; and
adjusting, by the processor, the gate distance to maintain the gate size in a predefined threshold.
8. A system (102) to recommend a guidance for ultrasound device settings, wherein the system comprises:
a memory (112); and
a processor (108) coupled to the memory (112), wherein the processor (108) is configured to execute instructions stored in the memory to:
receive an ultrasound image in real-time;
detect a region of interest of the ultrasound image based on an analysis of the ultrasound image using deep learning model;
identify vessel boundaries in the region of interest, wherein the vessel boundaries are identified based on an analysis of the region of interest of the ultrasound image using the deep learning model, wherein the vessel boundaries are identified based on vessel segmentation;
identify doppler structures and doppler parameters using a machine learning technique upon detection of the vessel boundaries, wherein the doppler structures comprise a color box, a doppler beam line, a doppler angle line and gate; and wherein the doppler parameters comprises a steering angle, a doppler angle, a gate position and a gate size;
determine a blood flow direction based on the vessel boundaries, the doppler structures and the doppler parameters;
determine an optimization in the doppler structures and the doppler parameters based on the blood flow direction, predefined doppler structures and predefined doppler parameters, wherein the optimization includes a change in steering angle, a change in position of the color box, a change in gate position, a change in doppler angle, and a change in gate size; and
recommend a guidance for an ultrasound device setting based on the optimization, wherein the recommendation is a visual recommendation or an audio recommendation.
9. The system (102) as claimed in claim 8, configured to optimally measure a blood flow velocity based on the recommendation.
10. The system (102) as claimed in claim 8, wherein the guidance is recommended using an Artificial Intelligence (AI) technique.
11. The system (102) as claimed in claim 8, wherein the change in steering angle comprises:
measuring an angle between the blood flow direction and the doppler beam line; and
adjusting the steering angle based on the angle.
12. The system (102) as claimed in claim 8, wherein the change in doppler angle comprises:
measuring a first angle between the blood flow direction and the doppler angle line;
measuring a second angle between the doppler angle line and the doppler beam line; and
aligning the doppler angle line to make the first angle minimum, and the second angle less than 60 degree.
13. The system (102) as claimed in claim 8, wherein the change in the gate position comprises:
determining a set of intersection points for the vessel boundaries and the doppler beam line;
measuring a distance between the gates and each intersection point; and
aligning gate position based on the distance.
14. The system (102) as claimed in claim 8, wherein the change in the gate size comprises:
measuring a gate distance between two lines of the gates; and
adjusting the gate distance to maintain the gate size in a predefined threshold.
15. A non-transitory computer program product having embodied thereon a computer program for recommending a guidance for ultrasound device settings, the computer program product storing instructions, the instructions comprising instructions for:
receiving an ultrasound image in real-time;
detecting a region of interest of the ultrasound image based on an analysis of the ultrasound image using deep learning model;
identifying vessel boundaries in the region of interest, wherein the vessel boundaries are identified based on an analysis of the region of interest of the ultrasound image using the deep learning model, wherein the vessel boundaries are identified based on vessel segmentation;
identifying doppler structures and doppler parameters using a machine learning technique upon detection of the vessel boundaries, wherein the doppler structures comprise a color box, a doppler beam line, a doppler angle line and gate; and wherein the doppler parameters comprises a steering angle, a doppler angle, a gate position and a gate size;
determining a blood flow direction based on the vessel boundaries, the doppler structures and the doppler parameters;
determining an optimization in the doppler structures and the doppler parameters based on the blood flow direction, predefined doppler structures and predefined doppler parameters, wherein the optimization includes a change in steering angle, a change in position of the color box, a change in gate position, a change in doppler angle, and a change in gate size; and
recommending a guidance for an ultrasound device setting based on the optimization, wherein the recommendation is a visual recommendation or an audio recommendation.