Technical field of the invention
[0001] The invention relates to the field of medical image analysis. More particularly, the invention relates to the diagnosis of a chest radiography of a patient to determine a health condition of the patient by applying a machine learning technology.
Background of the invention
[0002] Chest radiograph of a patient is an effective approach in health examination for diagnosis and evaluation of a spectrum of diseases. In particular, lung diseases can be identified and distinguished based on the manifestations of the lung diseases in the chest radiograph of the patient. By involving computer aided diagnosis using artificial intelligence for analysis of the manifestations of the lung diseases in the chest radiograph of the patient, a medical practitioner can easier provide precise further treatment to the patient. Since the manifestations of the lung diseases in the chest radiograph are similar, the correct distinction of the lung diseases is essential. Thus, there remains a need for an initial sensitive and specific identification and distinction of different lung diseases in the digital chest radiograph of the patient for further accurate treatment by the medical practitioner.
Summary of invention
[0003] This summary is provided to introduce a selection of concepts in a simplified form that are further disclosed in the detailed description of the invention. This summary is not intended to identify key or essential inventive concepts of the claimed subject matter, nor is it intended for determining the scope of the claimed subject matter.
[0004] The present invention discloses a system for diagnosis of a digital chest radiograph of a patient. The system comprises a processor; a non-transitory computer readable storage medium communicatively coupled to the processor, the non-transitory computer readable storage medium configured to store processor-executable instructions, which on execution, cause the processor to

receive the digital chest radiograph of the patient; locate a plurality of candidate objects in the digital chest radiograph using a classifier technique; determine a plurality of candidate object features for each of the located candidate objects using the classifier technique, wherein the candidate object features of a candidate object are a size range of the candidate object and a shape of the candidate object; decide a presence or an absence of at least one medical condition and a corresponding intensity level of each of the medical condition based on the determined candidate object features of each of the located candidate object using the classifier technique; and establish a relation between the presence of each of the at least one medical conditions and a value of each of a plurality of patient-related parameters by using the classifier technique, wherein applying the classifier technique on the received digital chest radiography to determine the value for each of the patient-related parameters corresponding to the patient.
[0005] The processor generates a report based on the presence of at least one medical condition, a corresponding intensity level of each of the present at least one medical condition and the established relation between the presence of each of the at least one medical condition and the value of each of the patient-related parameters. The candidate objects are a plurality of abnormality indicators, a plurality of anatomical features and/or a plurality of artifacts. Here, the abnormality indicator is an abnormal pattern in the anatomical feature indicating a presence of a medical condition. The anatomical feature represents a structure of the body of the patient such as the lungs, heart, chest wall, great vessels, etc. The medical condition represents a disease an injury, a presence of a foreign object, etc. The abnormality indicator is, for example, a lung nodule, a micro calcification, etc. The artifact is, for example, a radiopaque article on or external to the patient such as a jewel, hair, etc., grid-related artifact, etc.
[0006] The candidate object features of the candidate object are a size range of the candidate object and a shape of the candidate object. The medical condition is, for example, pneumococcal pneumonia, mediastinal tumor, lung cancer, pulmonary embolism, interstitial pulmonary edema, eosinophilic granuloma, sarcoidosis, usual interstitial pneumonitis (UIP), miliary tuberculosis, lymphangitic metastatic tumor, silicosis, scleroderma, pneumocystis pneumonia, etc. The patient-

related parameters corresponding to the patient are an age of the patient, a gender of the patient, a lifestyle and environmental condition of the patient and the like.
Brief description of the drawings
[0007] The present invention is described with reference to the accompanying figures. The accompanying figures, which are incorporated herein, are given by way of illustration only and form part of the specification together with the description to explain the make and use the invention, in which,
[0008] Figure 1 illustrates a block diagram of a system for diagnosis of a digital chest radiograph of a patient in accordance with the invention;
[0009] Figure 2 exemplarily illustrates a screenshot of a report on a graphical user interface of the system;
[0010] Figure 3 exemplarily illustrates the architecture of a computer system employed for implementing embodiments of the present disclosure; and
[0011] Figure 4 illustrates a flowchart for diagnosis of the digital chest radiograph of the patient in accordance with the invention.
Detailed description of the invention
[0012] Figure 1 illustrates a block diagram of a system 103 for diagnosis of a digital chest radiograph of a patient in accordance with the invention. The system comprises a processor 103c; a non-transitory computer readable storage medium 104a communicatively coupled to the processor 103c, the non-transitory computer readable storage medium 104a configured to store processor-executable instructions, which on execution, cause the processor 103c to receive the digital chest radiograph of the patient; locate a plurality of candidate objects in the digital chest

radiograph using a classifier technique; determine a plurality of candidate object features for each of the located candidate objects using the classifier technique, wherein the candidate object features of a candidate object are a size range of the candidate object and a shape of the candidate object; decide a presence or an absence of at least one medical condition and a corresponding intensity level of each of the medical condition based on the determined candidate object features of each of the located candidate object using the classifier technique; and establish a relation between the presence of each of the at least one medical conditions and a value of each of a plurality of patient-related parameters by using the classifier technique, wherein applying the classifier technique on the received digital chest radiography to determine the value for each of the patient-related parameters corresponding to the patient.
[0013] The processor 103c generates a report based on the presence of at least one medical condition, a corresponding intensity level of each of the present at least one medical condition and the established relation between the presence of each of the at least one medical condition and the value of each of the patient-related parameters. The candidate object in the digital chest radiograph is, for example, an abnormality indicator, an anatomical feature or an artifact. The anatomical feature represents a structure of the body of the patient such as the lungs, heart, chest wall, great vessels, rib cage, rib bones, etc. The abnormality indicator is an abnormal pattern in the anatomical feature indicating a presence of a medical condition. The abnormality indicator is, for example, a lung nodule, a micro calcification, a rib fracture, etc. The medical condition represents a disease, an injury, a presence of a foreign object, etc. The artifact is, for example, a radiopaque article on or external to the patient such as a jewel, hair, etc., grid-related artifact, etc.
[0014] The candidate object features of the candidate object are a size range of the candidate object and a shape of the candidate object. In an embodiment, the processor 103c specifies a presence or an absence of at least one medical condition and a corresponding intensity level of each of the at least one medical condition in the report. In another embodiment, the processor 103c specifies a presence of at least one medical condition and a corresponding intensity level of each of the present at least one medical condition. The medical condition is a disease such as, for example, pneumococcal pneumonia, mediastinal tumor, lung cancer, pulmonary embolism, interstitial

pulmonary edema, eosinophilic granuloma, sarcoidosis, usual interstitial pneumonitis (UIP), miliary tuberculosis, lymphangitic metastatic tumor, silicosis, scleroderma, pneumocystis pneumonia, etc., an injury such as a rib fracture, etc. In an embodiment, the processor 103c specifies a presence of at least one medical condition and a corresponding intensity level of each of the present at least one medical condition. The patient-related parameters corresponding to the patient are an age of the patient, a gender of the patient, a lifestyle and environmental condition of the patient, etc.
[0015] In an example, the processor 103c displays a chart or a table comprising a list of one or more medical conditions present in the digital chest radiograph of the patient and a corresponding intensity level of each of the medical condition present in the digital chest radiograph of the patient via the GUI 103d to the user. In another example, the processor 103c displays a chart or a table comprising a list of medical conditions, a state for each medical condition indicating either a presence or absence of the medical condition, and a corresponding intensity level of each of the medical condition via the GUI 103d to the user. If a medical condition is not present, then the corresponding intensity level is denoted as zero.
[0016] As used herein, the term “patient” refers to an individual receiving or registered to receive medical treatment. The patient is, for example, an individual undergoing a regular health checkup, an individual with a condition of tuberculosis, etc. As used herein, the term “digital chest radiography” refers to a two-dimensional or a three-dimensional array of digital image data projecting the contents of the chest and nearby anatomical structures, however, this is merely illustrative and not limiting of the scope of the invention.
[0017] In an embodiment, the system 103 is implemented as a web application implemented on a web based platform, for example, a website hosted on a server or a setup of servers. The web based platform hosts the web based application which is accessible to one or more user devices 101a, 101b or 101c. The user device 101a, 101b or 101c is, for example, a personal computer, a laptop, a tablet computing device, a personal digital assistant, a client device, a web browser, a pair of smart glasses, a smart contact lens, an end-to-end augmented reality device, etc. In an example,

the user device 101a, 101b or 101c is accessible over a network 102 such as the internet, a mobile telecommunication network, a Wi-Fi® network of the Wireless Ethernet Compatibility Alliance, Inc., etc.
[0018] In another embodiment, the system 103 is implemented as a software application, for example, a mobile application downloadable by a user on the user device 101a, 101b or 101c, for example, a tablet computing device, a mobile phone, a device with an end to end augmented or virtual reality interface, etc. As used herein, the term “user” is an individual who operates the software application to process the digital chest radiography of the patient and generate the report resulting from the processing of the digital chest radiography. The terms “user” and “patient” are used interchangeably herein.
[0019] In another embodiment, the system 103 is implemented as an embedded solution in a medical device. The embedded solution enables the user to focus on candidate objects for better analysis.
[0020] In an embodiment, the system 103 comprises a radiographic image capturing means 103a to capture the digital chest radiograph of the patient. The digital chest radiograph of the patient is an input to the system 103. The radiographic image capturing means 103a enables the user of the system 103 to capture the digital chest radiograph of the patient. In another embodiment, the system 103 comprises a reception means 103b adapted to receive the digital chest radiograph of the patient from a radiographic image capturing device 105. The radiographic image capturing device 105 is in communication with the system 103 via the network 102. In another embodiment, the reception means 103b receives a plurality of digital chest radiographs of the patient. As used herein, the term “radiographic image capturing device 105” refers to a device for capturing the digital chest radiograph of the patient. In an example, the radiographic image capturing device 105 is a portable digital radiographic machine. In another example, the radiographic image capturing device 105 is a smart mobile phone capable of capturing the digital chest radiographic images of the patient.

[0021] The radiographic image capturing device 105 is in communication with the system 103 via the network 102, for example, the internet, an intranet, a wireless network, a wired network, a Wi-Fi® network of the Wireless Ethernet Compatibility Alliance, Inc., a universal serial bus (USB) communication network, a ZigBee® network of ZigBee Alliance Corporation, a general packet radio service (GPRS) network, a global system for mobile (GSM) communications network, a code division multiple access (CDMA) network, a third generation (3G) mobile communication network, a fourth generation (4G) mobile communication network, a wide area network, a local area network, an internet connection network, an infrared communication network, etc., or any combination of these networks.
[0022] The system 103 comprises a graphical user interface (GUI) 103d comprising a plurality of interactive elements 103e configured to enable the processing of the digital chest radiography. As used herein, the term “interactive elements 103e” refers to interface components on the GUI 103d configured to perform a combination of processes, for example, a retrieval process, for example, retrieval of the digital chest radiography of the patient from a non-transitory computer readable storage medium 104a, processes that enable real time user interactions, etc. The interactive elements 103e comprise, for example, clickable buttons, a dropdown menu, a navigation menu, a help popup, etc.
[0023] In an example, the processor 103c accesses the radiographic image capturing device 105 to receive the digital chest radiograph of the patient. The processor 103c requests the radiographic image capturing device 105 for a permission to control the activities of the radiographic image capturing device 105 to capture the digital chest radiograph associated with the patient. The radiographic image capturing device 105 responds to the request received from the processor 103c. The processor 103c receives the response of the radiographic image capturing device 105.
[0024] The radiographic image capturing device 105 permits the user of the processor 103c to control the activities of the radiographic image capturing device 105 via the interactive elements 103e of the GUI 103d. As used herein, the term “activities” refer to focusing a field of view of the patient, adjusting a radiographic exposure, capturing the digital chest radiograph of the patient,

etc. Once the processor 103c has the permission to control the activities of the radiographic image capturing device 105, the user of the system 103 can view the input of the radiographic image capturing device 105 on the screen of the GUI 103d. The user can focus the field of view to observe the chest area of the patient by using with the interactive elements 103e via a user input device such as a mouse, a trackball, a joystick, etc. The user captures the digital chest radiograph of the patient using the interactive elements 103e of the GUI 103d.
[0025] In an embodiment, the processor 103c adaptably controls the activities specific to the radiographic image capturing device 105 based on a plurality of parameters of the radiographic image capturing device 105. The parameters of the radiographic image capturing device 105 are, for example, a version, a manufacturer, model details, etc., of the radiographic image capturing device 105. The system 103 is customizable to suit the parameters of the radiographic image capturing device 105. In other terms, the system 103 is customizable and can be suitable adapted to capture the digital chest radiograph of the patient for different manufacturers and/or versions of the radiographic image capturing device 105.
[0026] In another embodiment, the user captures a chest radiographic film of the patient using an image capturing device, for example, a camera, a smartphone with a camera, etc., to convert the chest radiographic film into a digital chest radiographic image. The digital chest radiographic image is the input to the system 103. Here, the term “digital chest radiographic image” is a two-dimensional array of digital image data. The image capturing device is in communication with the system 103 via the network 102. The reception means 103b receives the digital chest radiographic image of the patient via the image capturing device.
[0027] In another embodiment, the input to the system 103 is an already existing digital chest radiographic image of the patient stored in the non-transitory computer readable storage medium 104a. Here, the term “digital chest radiographic image” is a two-dimensional or three-dimensional array of digital image data. The digital chest radiographic image is a digital representation of the chest radiograph of the patient recorded on the chest radiographic film. The chest radiographic film comprises a recorded image of the chest radiograph of the patient.

[0028] In an embodiment, the reception means 103b receives information associated with the patient from the user via the GUI 103d. The information associated with the patient is, for example, personal details about the patient, medical condition of the patient, etc. In an embodiment, the non-transitory computer readable storage medium 104a is also configured to store patient profile information, patient medical history, reports of the patients, etc.
[0029] As used herein, the term “classifier technique” refers to a class of deep artificial neural network, for example, a convolutional neural network, that can be applied to analyzing visual imagery. The convolutional neural network corresponds to a specific model of an artificial neural network. In an embodiment, one or more convolutional neural networks are applied to process the digital chest radiograph of the patient. The classifier technique may also comprise, for example, a support vector machine, recurrent neural networks, deep belief networks, a random forest, gradient boosting, decision trees, boosted decision trees, partial least square classification or regression, branch-and-bound algorithms, neural network models, deep neural networks, convolutional deep neural networks or any combination thereof.
[0030] The processor 103c is adapted to locate multiple candidate objects in the digital chest radiograph using the classifier technique. For example, the processor 103c locates abnormality indicators, anatomical features and/or any artifacts present in the digital chest radiograph using the classifier technique.
[0031] The processor 103c determines the candidate object features for each of the located candidate objects in the digital chest radiograph using the classifier technique. The candidate object features of a candidate object are the size range of the candidate object and the shape of the candidate object.
[0032] In an embodiment, the processor 103c highlights the candidate object features, for example, shape, of each of the located candidate objects in the digital chest radiograph. For example, the processor 103c denotes each category of candidate object with a predetermined pixel intensity

using the classifier technique. The classifier technique is pre-trained to identify each category of candidate object with a predetermined pixel intensity. Here, the categories of candidate objects are, for example, the multiple abnormalities, the multiple anatomical features and the multiple artefacts which are determined by the classifier technique. For example, the processor 103c emphasizes the abnormalities like distorted hilar structures, cavities, etc., and anatomical features such as airways, lung zones, lung lobes, diaphragm, etc., in the digital chest radiograph with different predetermined pixel intensity. The processor 103c highlights the shape, size and location of each of the located candidate objects in the digital chest radiograph. This makes further effective analysis of the digital chest radiograph easier for the medical practitioner.
[0033] The processor 103c computes a presence or an absence of at least one medical condition and a corresponding intensity level of each of the medical condition based on the determined candidate object features of each of the located candidate object using the classifier technique. The medical condition is, for example, pneumococcal pneumonia, mediastinal tumor, lung cancer, pulmonary embolism, interstitial pulmonary edema, eosinophilic granuloma, sarcoidosis, usual interstitial pneumonitis (UIP), miliary tuberculosis, lymphangitic metastatic tumor, silicosis, scleroderma, pneumocystis pneumonia, emphysema, a rib fracture, a heart condition, etc.
[0034] The processor 103c generates the report based on the presence of at least one medical condition, a corresponding intensity level of each of the present at least one medical condition and the established relation between the presence of each of the at least one medical condition and the value of each of the patient-related parameters. The processor 103c applies the classifier technique on the received digital chest radiography to determine the value of each of the patient-related parameters corresponding to the patient. The patient-related parameters corresponding to the patient are an age of the patient, a gender of the patient, a lifestyle and environmental condition of the patient, etc. The classifier technique analyses the digital chest radiograph to determine the patient-related parameters corresponding to the patient. The lifestyle and environmental conditions comprises one or more of a smoking condition of the patient, a drug addiction of the patient, an occupation of the patient, a pollution level of a location of the patient, etc.

[0035] In an embodiment, the classifier technique classifies the age of the patient as either “child” or “adult” based on the digital chest radiograph of the patient. Here, the values of age are “child” and “adult”. In another embodiment, the classifier technique classifies the gender of the patient as either “male” or “female” for an adult. In another embodiment, the classifier technique classifies the lifestyle and environmental conditions of the patient as “low”, “medium” or “high” based on a degree of healthy lifestyle and environmental conditions from the analysis of the digital chest radiograph. For example, a smoking and drug addict has abnormalities in the chest region. The processor 103c analyses the digital chest radiograph of the smoking and drug addict and determines the lifestyle and environmental conditions as “low”.
[0036] For each of the medical condition present in the digital chest radiograph of the patient, the processor 103c establishes a relation between the value of each of the patient-related parameters and the presence of each of the medical conditions in the patient by using the classifier technique. The relation between a value of a patient-related parameter and a medical condition present in the digital chest radiograph of the patient indicates whether the patient-related parameter is a probable cause for the presence of the medical condition. The processor 103c identifies probable one or more patient-related parameters causing the medical condition in the patient using the classifier technique. The classifier technique is pre-trained to identify the probable causes for a medical condition. The processor 103c, thus establishes the relation between the value of each of the determined patient-related parameters of the patient and each of the medical conditions present in the digital chest radiograph.
[0037] The patient-related parameter is either “a probable cause” or “an improbable cause” for the presence of the medical condition in the digital chest radiograph of the patient. For example, the patient-related parameter which is a probable cause for the presence of the medical condition is mentioned as “a probable cause” in the report. The processor 103c displays the report for the digital chest radiograph via the GUI 103d. In an embodiment, the report comprises the presence of any medical conditions in the patient, the value of each of the patient-related parameters corresponding to the patient and a relation between the each of the patient-related parameters and the medical conditions present in the digital chest radiograph of the patient. In an embodiment, the processor

103c provides suggestions, for example, diet, exercise routine, regular medical check-ups, etc., based on the relation between the value of each of the patient-related parameters and the medical conditions present in the digital chest radiograph of the patient.
[0038] In an example, the report is provided to the user as suitable messages via a pop-up box displayed on a screen. In another example, the GUI 103d is a smart glasses with augmented reality/virtual reality capabilities. The report is displayed in three-dimensional GUI 103d such as an augmented reality or virtual reality. In an embodiment, the processor 103c communicates the report to the patient via an electronic mail. The processor 103c also stores the report in the non-transitory computer readable storage medium 104a of the system 103.
[0039] In an embodiment, when the processor 103c decides the presence of no medical conditions in the digital chest radiograph of the patient using the classifier technique, then the processor 103c determines the value of each of the patient-related parameters corresponding to the patient using the classifier technique and generates the report. That is, the processor 103c terminates the step of establishment of the relation between the value of each of the determined patient-related parameters of the patient and any of the medical conditions since no medical condition is present in the digital chest radiograph. In other words, for a healthy subject, the processor 103c decides no medical condition to be present in the digital chest radiograph of the healthy subject using the classifier technique and the processor 103c determines the value of each of the patient-related parameters by analyzing the digital chest radiograph using the classifier technique. The report comprises a note stating no medical conditions are present along with the value of each of the patient-related parameters corresponding to the healthy subject.
[0040] Figure 2 exemplarily illustrates the screenshot of the report on the GUI 103d of the system 103. The report comprises the digital chest radiograph of the patient and a digital chest radiograph of the patient with the highlighted candidate object features of each of the located candidate objects in the digital chest radiograph. The report comprises the one or more medical conditions present in the digital chest radiograph of the patient. As shown in the Figure 2, the report indicates the presence of a Disease C in the digital chest radiograph of the patient with the intensity level of ‘2’.

The report comprises a computed value of each of the patient-related parameters corresponding to the patient and a relation between the value of each of the patient-related parameters and the Disease C. The relation between the value of each of the patient-related parameters and the Disease C denotes whether a patient-related parameter is a probable or an improbable cause for the presence of the Disease C. The report further comprises suggestions based on the relation between the value of each of the patient-related parameters and the intensity of the Disease C. The “Identified values of patient-related parameters in the digital chest radiograph” are calculated by the processor 103c using the classifier technique. The relation between the value of each of the patient-related parameters and the Disease C is computed by the processor 103c using the classifier technique. The report summary also comprises the digital chest radiograph of the patient and the analyzed digital chest radiograph of the patient with the candidate objects associated with the Disease C.
[0041] Consider for example, the processor 103c is used for the diagnosis of a specific medical condition, for instance, tuberculosis, using the digital chest radiograph of the patient. The user of the system 103 selects tuberculosis from a list of medical conditions displayed via the GUI 103d. The user of the system 103 uploads one or more additional test results associated with the patient via the GUI 103d. The processor 103c receives the one or more additional test results associated with the patient for tuberculosis such as a blood test result, a skin test result, a sputum test result, a microscopic-observation drug-susceptibility test result, a semen sample assay, etc. The processor 103c locates the candidate objects in the digital chest radiograph using the classifier technique; determines the candidate object features for each of the located candidate objects using the classifier technique; identifies the presence or the absence of tuberculosis and a corresponding intensity level of tuberculosis using the classifier technique. The processor 103c determines the value of each of the patient-related parameters corresponding to the patient using the classifier technique.
[0042] If the processor 103c identifies the presence of tuberculosis using the classifier technique, then the processor 103c generates the report with the intensity level of tuberculosis, the value of each of the patient-related parameters corresponding to the patient and the probable patient-related

parameters causing tuberculosis in the patient. Further, the processor 103c also adds the received one or more additional test results associated with the patient for tuberculosis in the report. The processor 103c displays the report to the user via the GUI 103d. The addition of the one or more additional test results in the report allows a medical practitioner to verify the multiple results and provide effective treatment to the patient.
[0043] If the processor 103c does not identifies the presence of tuberculosis using the classifier technique, then the processor 103c generates the report stating the absence of tuberculosis in the digital chest radiograph of the patient. The processor 103c computes the value of each of the patient-related parameters corresponding to the patient. The processor 103c displays the report to the user via the GUI 103d. The report comprises the status of tuberculosis in the digital chest radiograph of the patient, the value of each of the patient-related parameters corresponding to the patient and the received one or more additional test results associated with the patient for tuberculosis.
[0044] In an embodiment, the processor 103c assesses a quality value of the digital chest radiograph before locating the candidate objects in the digital chest radiograph using the classifier technique. The processor 103c assesses the quality value of the digital chest radiograph using the classifier technique. The processor 103c continues with the locating of the candidate objects in the digital chest radiograph using the classifier technique only when the quality value of the digital chest radiograph is above a threshold level. The processor 103c discards the digital chest radiograph when the quality value of the digital chest radiograph is below a threshold level. The quality value of the digital chest radiograph defines an overall grading efficiency of the digital chest radiograph based on a plurality of quality parameters. The quality parameters are, for example, darkness, light, contrast, color accuracy, tone reproduction, distortion, sharpness, noise, etc. The threshold level is defined by, for example, an annotator during the training of the classifier technique.
[0045] The processor 103c trains the classifier technique to assess the quality value of the digital chest radiograph based on the quality parameters. The processor 103c trains the classifier

technique to measure the quality parameters for the digital chest radiograph. For example, the quality value of the digital chest radiograph is “good” or “bad” based on a predetermined number of measured quality parameters above or below the threshold level. However, this example is merely illustrative and not limiting of the scope of the invention.
[0046] Figure 3 exemplarily illustrates the architecture of a computer system employed for implementing embodiments of the present disclosure. The system 103 exemplarily illustrated in Figure 1 employs the architecture of the computer system 300 exemplarily illustrated in Figure 3. The computer system 200 is programmable using a high level computer programming language. The computer system 200 may be implemented using programmed and purposeful hardware.
[0047] The network 102 is, for example, the internet, a local area network, a wide area network, a wired network, a wireless network, a mobile communication network, etc. The computer system 300 comprises, for example, a processor 301, a memory unit 302 for storing programs and data, an input/output (I/O) controller 303, a network interface 304, a data bus 305, a display unit 306, input devices 307, fixed disks 308, removable disks 309, output devices 310, etc.
[0048] As used herein, the term “processor” 301 refers to any one or more central processing unit (CPU) devices, microprocessors, an application specific integrated circuit (ASIC), computers, microcontrollers, digital signal processors, logic, an electronic circuit, a field-programmable gate array (FPGA), etc., or any combination thereof, capable of executing computer programs or a series of commands, instructions, or state transitions. The processor 301 may also be realized as a processor set comprising, for example, a math or graphics co-processor and a general purpose microprocessor. The processor 301 is selected, for example, from the Intel® processors such as the Itanium® microprocessor or the Pentium® processors, Advanced Micro Devices (AMD®) processors such as the Athlon® processor, MicroSPARC® processors, UltraSPARC® processors, hp® processors, International Business Machines (IBM®) processors, the MIPS® reduced instruction set computer (RISC) processor, Inc., RISC based computer processors of ARM Holdings, etc. The system 103 disclosed herein is not limited to a computer system 300 employing a processor 301 but may also employ a controller or a microcontroller.

[0049] The memory unit 302 is used for storing data, programs, and applications. The memory unit 302 is, for example, a random access memory (RAM) or any type of dynamic storage device that stores information for execution by the processor 301. The memory unit 302 also stores temporary variables and other intermediate information used during execution of the instructions by the processor 301. The computer system 300 further comprises a read only memory (ROM) or another type of static storage device that stores static information and instructions for the processor 301.
[0050] The I/O controller 303 controls input actions and output actions performed by the system 103. The network interface 304 enables connection of the computer system 300 to the network 102. The network interface 304 comprises, for example, one or more of a universal serial bus (USB) interface, a cable interface, an interface implementing Wi-Fi® of the Wireless Ethernet Compatibility Alliance, Inc., a FireWire® interface of Apple, Inc., an Ethernet interface, a digital subscriber line (DSL) interface, a token ring interface, a peripheral controller interconnect (PCI) interface, a local area network (LAN) interface, a wide area network (WAN) interface, interfaces using serial protocols, interfaces using parallel protocols, and Ethernet communication interfaces, asynchronous transfer mode (ATM) interfaces, interfaces based on transmission control protocol (TCP)/internet protocol (IP), radio frequency (RF) technology, etc. The data bus 305 permits communications between the means/modules (103a, 103b, 103c, 103d and 103e) of the system 103.
[0051] The display unit 306, via the GUI 103d, displays information, display interfaces, interactive elements 103e such as drop down menus, text fields, checkboxes, text boxes, floating windows, hyperlinks, etc., for example, for allowing the user to enter inputs associated with the patient. In an example, the display unit 306 comprises a liquid crystal display, a plasma display, etc. The input devices 307 are used for inputting data into the computer system 300. For example, a user may enter a patient’s profile information, the patient’s medical history, etc., using the input devices 307. The input devices 307 are, for example, a keyboard such as an alphanumeric

keyboard, a touch pad, a joystick, a computer mouse, a light pen, a physical button, a touch sensitive display device, a track ball, etc.
[0052] Computer applications and programs are used for operating the computer system 300. The programs are loaded onto the fixed disks 308 and into the memory unit 302 of the computer system 300 via the removable disks 309. In an embodiment, the computer applications and programs may be loaded directly via the network 102. The output devices 310 output the results of operations performed by the system 103.
[0053] The processor 301 executes an operating system, for example, the Linux® operating system, the Unix® operating system, any version of the Microsoft® Windows® operating system, the Mac OS of Apple Inc., the IBM® OS/2, VxWorks® of Wind River Systems, Palm OS®, the Solaris operating system, the Android operating system, Windows Phone™ operating system developed by Microsoft Corporation, the iOS operating system of Apple Inc., etc.
[0054] The computer system 300 employs the operating system for performing multiple tasks. The operating system is responsible for management and coordination of activities and sharing of resources of the computer system 300. The operating system employed on the computer system
300 recognizes, for example, inputs provided by the user using one of the input devices 307, the output display, files, and directories stored locally on the fixed disks 308. The operating system on the computer system 300 executes different programs using the processor 301. The processor
301 and the operating system together define a computer platform for which application programs in high level programming languages are written.
[0055] The processor 301 retrieves instructions for executing the modules (103a, 103b, 103c, 103d and 103e) of the system 103 from the memory unit 302. A program counter determines the location of the instructions in the memory unit 302. The program counter stores a number that identifies the current position in the program of each of the modules (103a, 103b, 103c, 103d and 103e) of the system 103. The instructions fetched by the processor 301 from the memory unit 302

after being processed are decoded. The instructions are stored in an instruction register in the processor 301. After processing and decoding, the processor 301 executes the instructions.
[0056] Figure 4 illustrates the flowchart for diagnosis of the digital chest radiograph of the patient in accordance with the invention. At step S1, the processor 103c receives the digital chest radiograph of the patient. The system 103 comprising the non-transitory computer readable storage medium 104a communicatively coupled to the processor 103c. The system 103 comprises the GUI 103d comprising multiple interactive elements 103e configured to enable capture and processing of the digital chest radiograph.
[0057] At step S2, the processor 103c locates the candidate objects in the digital chest radiograph using classifier technique. The candidate object in the digital chest radiograph is, for example, an abnormality indicator, an anatomical feature, an artifact or the like. Here, the abnormality indicator is an abnormal pattern in the anatomical feature indicating a presence of a medical condition. The medical condition represents a disease. The abnormality indicator is, for example, a lung nodule, a micro calcification, etc. The anatomical feature represents a structure of the body of the patient such as the lungs, heart, chest wall, great vessels, etc.
[0058] At step S3, the processor 103c determines the candidate object features for each of the located candidate objects using the classifier technique. The candidate object features of a candidate object are the size range of the candidate object and the shape of the candidate object. At step S4, the processor 103c decides the presence or the absence of the at least one medical condition and a corresponding intensity level of each of the medical condition based on the determined candidate object features of each of the located candidate object using the classifier technique. The medical condition is, for example, pneumococcal pneumonia, mediastinal tumor, lung cancer, pulmonary embolism, interstitial pulmonary edema, eosinophilic granuloma, sarcoidosis, usual interstitial pneumonitis (UIP), miliary tuberculosis, lymphangitic metastatic tumor, silicosis, scleroderma, pneumocystis pneumonia, etc.

[0059] At step S5, the processor 103c establishes the relation between the presence of each of the at least one medical conditions and the value of each of the patient-related parameters by using the classifier technique. The classifier technique considers the intensity level of a medical condition present in the digital chest radiograph to establish relations between the medical condition present and the value of each of the patient-related parameters. The processor 103c applies the classifier technique on the received digital chest radiography to determine the value for each of the patient-related parameters corresponding to the patient. The patient-related parameters corresponding to the patient are an age of the patient, a gender of the patient, a lifestyle and environmental condition of the patient, etc. The report comprises the presence of any medical conditions in the patient and the value of each of the patient-related parameters corresponding to the patient patient-related parameters corresponding to the patient. The processor 103c generates the report based on the presence of at least one medical condition, a corresponding intensity level of each of the present at least one medical condition and the established relation between the presence of each of the at least one medical condition and the value of each of the patient-related parameters.
[0060] The system 103 reduces errors resulting from manual identification of various medical conditions during screening of the patient. The system 103 acts as an important supporting tool in detecting/monitoring one or more diseases, in specific, interstitial lung diseases and/or or a response to a therapy. The system 103 reduces the time-consumption involved in a manual recording of the medical conditions present/absent in the digital chest radiograph of the patient. The system 103 acts as a support tool in identifying possible causes resulting in a current medical condition of the patient. The system 103 is not limited to providing the current medical condition of the patient but also provides recommendations for improving the patient health in the form of suggestions based on the established relation between the presence of each of the at least one medical condition and each of the patient-related parameters. The system 103 thus, plays a significant role in the overall health and wellness of the patient. The system 103 acts as a personalized health and wellness management platform by providing suggestions based on the patient-related parameters and the presence of at least one medical condition in the digital chest radiograph of the patient. If no medical condition is present, then the system 103 calculates the

value of each of the patient-related parameters and provides suggestions based on the value of the patient-related parameters.
[0061] The present invention described above, although described functionally or sensibly, may be configured to work in a network environment comprising a computer in communication with one or more devices. It will be readily apparent that the various methods, algorithms, and computer programs disclosed herein may be implemented on computer readable media appropriately programmed for general purpose computers and computing devices. As used herein, the term “computer readable media” refers to non-transitory computer readable media that participate in providing data, for example, instructions that may be read by a computer, a processor or a similar device. Non-transitory computer readable media comprise all computer readable media. Non-volatile media comprise, for example, optical discs or magnetic disks and other persistent memory volatile media including a dynamic random access memory (DRAM), which typically constitutes a main memory. Volatile media comprise, for example, a processor cache, a register memory, a random access memory (RAM), etc. Transmission media comprise, for example, coaxial cables, copper wire, fiber optic cables, modems, etc., including wires that constitute a system bus coupled to a processor, etc. Common forms of computer readable media comprise, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, a Blu-ray Disc®, a magnetic medium, a compact disc-read only memory (CD-ROM), a digital versatile disc (DVD), any optical medium, a flash memory card, a laser disc, RAM, a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), a flash memory, any other cartridge, etc.
[0062] The non-transitory computer readable storage medium 104a is, for example, a structured query language (SQL) data base or a not only SQL (NoSQL) data base such as the Microsoft® SQL Server®, the Oracle® servers, the MySQL® non-transitory computer readable storage medium of MySQL AB Company, the MongoDB® of 10gen, Inc., the Neo4j graph non-transitory computer readable storage medium , the Cassandra non-transitory computer readable storage medium of the Apache Software Foundation, the HBase™ non-transitory computer readable storage medium of the Apache Software Foundation, etc. In an embodiment, the non-transitory

computer readable storage medium 104a can also be a location on a file system. The non-transitory computer readable storage medium 104a is any storage area or medium that can be used for storing data and files. In another embodiment, the non-transitory computer readable storage medium 104a can be remotely accessed by the system 103 via the network 102. In another embodiment, the non-transitory computer readable storage medium 104a a is configured as a cloud based non-transitory computer readable storage medium 104a implemented in a cloud computing environment, where computing resources are delivered as a service over the network 102, for example, the internet.
[0063] The foregoing examples have been provided merely for the purpose of explanation and does not limit the present invention disclosed herein. While the invention has been described with reference to various embodiments, it is understood that the words are used for illustration and are not limiting. Those skilled in the art, may effect numerous modifications thereto and changes may be made without departing from the scope and spirit of the invention in its aspects.

CLAIMS
We claim:
1. A system 103 for diagnosis of a digital chest radiograph of a patient, comprising:
a processor 103c;
a non-transitory computer readable storage medium 104a communicatively coupled to the processor 103c, the non-transitory computer readable storage medium 104a configured to store processor-executable instructions, which on execution, cause the processor 103c to
receive the digital chest radiograph of the patient;
locate a plurality of candidate objects in the digital chest radiograph using a classifier technique;
determine a plurality of candidate object features for each of the located candidate objects using the classifier technique, wherein the candidate object features of a candidate object are a size range of the candidate object and a shape of the candidate object;
decide a presence or an absence of at least one medical condition and a corresponding intensity level of each of the medical condition based on the determined candidate object features of each of the located candidate object using the classifier technique; and
establish a relation between the presence of each of the at least one medical conditions and a value of each of a plurality of patient-

related parameters by using the classifier technique, wherein applying the classifier technique on the received digital chest radiography to determine the value for each of the patient-related parameters corresponding to the patient.
2. The system 103 as claimed in claim 1, wherein the processor 103c generates a report based on the presence of at least one medical condition, a corresponding intensity level of each of the present at least one medical condition and the established relation between the presence of each of the at least one medical condition and the value of each of the patient-related parameters.
3. The system 103 as claimed in claim 1, wherein the medical condition is pneumococcal pneumonia, mediastinal tumor, lung cancer, pulmonary embolism, interstitial pulmonary edema, eosinophilic granuloma, sarcoidosis, usual interstitial pneumonitis (UIP), miliary tuberculosis, lymphangitic metastatic tumor, silicosis, scleroderma, pneumocystis pneumonia or the like.
4. The system 103 as claimed in claim 1, the patient-related parameters corresponding to the patient are an age of the patient, a gender of the patient and a lifestyle and environmental condition of the patient.
5. The system 103 as claimed in claim 1, wherein the candidate objects are a plurality of abnormality indicators, a plurality of anatomical features and a plurality of artifacts.
6. A method for diagnosis of a digital chest radiograph of a patient using a system 103, said method comprising:
receiving the digital chest radiograph of the patient;
locating a plurality of candidate objects in the digital chest radiograph using a classifier technique;

determining a plurality of candidate object features for each of the located candidate objects using the classifier technique, wherein the candidate object features of a candidate object are a size range of the candidate object and a shape of the candidate object;
deciding a presence or an absence of at least one medical condition and a corresponding intensity level of each of the medical condition based on the determined candidate object features of each of the located candidate object using the classifier technique; and
establishing a relation between the presence of each of the at least one medical conditions and a value of each of a plurality of patient-related parameters by using the classifier technique, wherein applying the classifier technique on the received digital chest radiography to determine the value for each of the patient-related parameters corresponding to the patient.
7. The method as claimed in claim 6, wherein generating a report based on the presence of at least one medical condition, a corresponding intensity level of each of the present at least one medical condition and the established relation between the presence of each of the at least one medical condition and the value of each of the patient-related parameters.
8. The method as claimed in claim 6, wherein the medical condition is pneumococcal pneumonia, mediastinal tumor, lung cancer, pulmonary embolism, interstitial pulmonary edema, eosinophilic granuloma, sarcoidosis, usual interstitial pneumonitis (UIP), miliary tuberculosis, lymphangitic metastatic tumor, silicosis, scleroderma, pneumocystis pneumonia or the like.
9. The method as claimed in claim 6, the patient-related parameters corresponding to the patient are an age of the patient, a gender of the patient, a lifestyle and environmental condition of the patient and/or the like.

10. The method as claimed in claim 6, wherein the candidate objects are a plurality of abnormality indicators, a plurality of anatomical features and a plurality of artifacts.