Artificial intelligence based portable x-ray imaging modality and method thereof

Abstract

An artificial intelligence based portable X-ray imaging modality and method thereof is disclosed. In one embodiment, an imaging modality includes a processing unit, an X-ray generator configured to generate an X-ray beam having energy range of 20keV to 120keV, an X-ray collimator configured to focus the X-ray beam on a target body part, and an X-ray detector configured to detect the X-rays passing through the target body part. The X-ray collimator is adapted to receive a filter screen to focus the X-ray beam on the target body part. The processing unit is configured to configure the X-ray generator to generate the X-ray beam having desired energy and wavelength based on the target body part, generate a high-quality digital radiograph of the target body part, and detect presence of structural deformity / infection in the target body part in real-time based on the digital radiograph using a trained artificial intelligence model.

Description

ARTIFICIAL INTELLIGENCE BASED PORTABLE X-RAY IMAGING MODALITY AND METHOD THEREOFPRIORITY CLAIM

[0001] This patent application claims priority from Indian Patent Application no. 202441004235 titled “PORTABLE RETROFIT FOR ON-SITE MOBILE RADIOLOGICAL DIAGNOSTICS AND REPORTING” filed on 22ndJanuary 2024 which is incorporated in entirety by reference.FIELD OF TECHNOLOGY

[0002] The present disclosure relates to the field of healthcare, and more particularly to artificial intelligence based portable X-ray imaging modality and a method thereof.BACKGROUND

[0003] In the field of healthcare, especially during regular healthcare and disaster relief situations, access to diagnostic tools, particularly radiological diagnostics, is essential for accurate assessment and treatment of injuries, including musculoskeletal injuries.

[0004] Conventional radiological diagnostic systems are bulky and require significant power sources to operate effectively. This makes them impractical for use during disaster relief situations or in remote areas where access to a reliable power source is limited. Furthermore, movement of these systems from hospital to a remote site can be challenging due to their size and weight, making them difficult to move around efficiently.

[0005] Also, ergonomics of current radiological diagnostic systems may not optimal for portable use, as they are often designed for stationary use. This results in additional challenges when attempting to carry them during disaster relief situations or while providing regular healthcare services in remote locations or home care scenarios.

[0006] Moreover, radiation exposure is a significant concern with traditional radiological diagnostic systems, which can lead to potential health risks for both medical professionals operating the equipment and individual undergoing medical imaging procedure. Existing radiological diagnostic systems have attempted to address the above challenge by improving radiation exposure control settings, however, this provision do not fully mitigate risks associated with prolonged or repeated exposure.

[0007] In light of the above, there exists a need for an off-grid, on-site, portable radiological diagnostic and reporting system with primary supportive treatment capabilities.SUMMARY

[0008] The scope of the present disclosure is defined solely by the appended claims and is not affected to any degree by the statements within this summary. The present embodiments may obviate one or more of the drawbacks or limitations in the related art.

[0009] An artificial intelligence-based X-ray imaging modality and method thereof is disclosed. In one aspect, a portable X-ray imaging modality includes a processing unit, an X- ray generator communicatively coupled to the processing unit. The X-ray generator is configured to generate an X-ray beam having energy range of 20keV to 120keV The imaging modality includes an X-ray collimator communicatively coupled to the processing unit. The X- ray collimator is configured to focus the X-ray beam on the target body part. The imaging modality includes an X-ray detector communicatively coupled to the processing unit. The X- ray detector is configured to detect the X-rays passing through the target body part. The X-ray collimator is adapted to receive a filter screen to focus the X-ray beam on the target body part. The processing unit is configured to configure the X-ray generator to generate the X-ray beam having desired energy and wavelength based on the target body part, generate a high-quality digital radiograph of the target body part based on the X-rays detected by the X-ray detector, and detect presence of structural deformity / infection in the target body part in real-time based on the high quality radiograph using a trained artificial intelligence model.

[0010] The processing unit may be configured to detect the target body part on which medical imaging procedure is to be performed, and determine configuration parameter values required to generate the X-ray beam of specific energy and wavelength for the detected target body part.

[0011] The processing unit may be configured to configure the X-ray generator to generate the X-ray beam having specific energy and wavelength based on the determine configuration parameter values. The processing unit may be configured to generate a digital radiograph of the target body parts based on the detected X-rays, and transform the digital radiograph into the high-quality digital radiograph of the target body part.

[0012] The processing unit may be configured to determine whether the high-quality digital radiograph captures the target body part on which medical imaging procedure is to be performed.

[0013] The processing unit may be configured to pre-process the high-quality digital radiograph of the target body part, apply the pre-processed high quality digital radiograph data to the trained artificial intelligence model, and determine presence of structural deformity / infection in the target body part using the trained artificial intelligence model.

[0014] The imaging modality may include a communication module configured to communicate the high-quality digital radiograph and patient data to a cloud computing platform associated with an hospital via a network. The imaging modality may include a display unit configured for displaying the high-quality digital radiograph of the target body part. The imaging modality may include a rechargeable battery pack configured to supply power to the X-ray generator.

[0015] In another aspect, a method of detecting structural deformity / infection in patient body using a portable X-ray imaging modality includes dynamically detecting a target body part on which medical imaging procedure is to be performed, and determining configuration parameter values to configure an X-ray generator based on the target body part, and configuring an X-ray generator to generate an X-ray beam having specific energy and wavelength based onthe configuration parameter values. The configuration parameter values include current and voltage values corresponding to specific energy and wavelength of X-ray beam to be focused on the target body part. The method includes generating a high-quality digital radiograph of the target body part based on the X-ray beam having the specific energy and wavelength focused on the target body part. The method includes detecting, using the processing unit, presence of structural deformity / infection in the target body part in real-time based on the high- quality radiograph using a trained artificial intelligence model.

[0016] In generating the high-quality digital radiograph of the target body part based on the X-ray beam having the specific energy and wavelength focused on the target body part, the method may include generating a digital radiograph of the target body parts based on the X-ray beam focused on the target body part, and transforming the digital radiograph into the high quality digital radiograph of the target body part.

[0017] The method may include determining whether the high-quality digital radiograph captures the target body part on which medical imaging procedure is to be performed.

[0018] In detecting presence of structural deformity / infection in the target body part in real-time based on the high quality radiograph using the trained artificial intelligence model, the method may include pre-processing the high quality digital radiograph of the target body part, applying the pre-processed high quality digital radiograph data to the trained artificial intelligence model, and determining presence of structural deformity / infection in the target body part using the trained artificial intelligence model.

[0019] The method may include sending the high-quality digital radiograph and patient data to a cloud computing platform associated with an hospital via the network.

[0020] In yet another aspect, a system includes at least one portable X-ray imaging apparatus configured to determine configuration parameter values to configure an X-ray generator, configure an X-ray generator to generate an X-ray beam having a specific energy and wavelength based on the configuration parameter values, generate a high quality digital radiograph of a target body part on which medical imaging procedure is to be performed based on the X-ray beam having specific energy and wavelength, and determine presence of structural deformity / infection in the target body part using a primary trained artificial intelligence model. The system includes a cloud computing platform communicatively coupled to the portable X- ray imaging modality. The cloud computing platform is configured to receive the high-quality digital radiograph of the target body part and patient data from the portable X-ray imaging modality, and detect type and severity of structural deformity / infection present in the target body part using a secondary trained artificial intelligence model based on the high quality digital radiograph.

[0021] The portable X-ray imaging modality may be configured to pre-process the high-quality digital radiograph of the target body part, apply the pre-processed high quality digital radiograph data to the primary trained artificial intelligence model, and determine presence of structural deformity / infection in the target body part using the primary trained artificial intelligence model.

[0022] The portable X-ray imaging modality may be configured to pre-process the high-quality digital radiograph of the target body part, apply the pre-processed high quality digital radiograph data to the secondary trained artificial intelligence model, and classify structural deformity / infection in the target body part using the secondary trained artificial intelligence model in terms of type of structural deformity / infection and severity.

[0023] The portable X-ray imaging modality may be configured to determine whether there exists the structural deformity / infection in the target body part using the primary trained artificial intelligence model based on the high-quality radiograph, and transmit the high-quality radiograph and patient data to the cloud computing platform if there exists the structural deformity / infection in the target body part.

[0024] The portable X-ray imaging modality may be configured to determine whether the high-quality digital radiograph captures the target body part on which medical imaging procedure is to be performed.

[0025] The cloud computing platform may be configured to generate at least one medical treatment for a patient based on the severity and type of structural deformity / infection.BRIEF DESCRIPTION OF DRAWINGS

[0026] The above-mentioned and other features will now be addressed with reference to the accompanying drawings of the present disclosure. The illustrated embodiments are intended to illustrate, but not limit the disclosure.

[0027] The drawings described herein are for illustrative purposes and are not intended to limit the scope of the present subject matter in any way:

[0028] FIG. 1 is a schematic diagram depicting a system for detecting structural deformity / infection in body of an individual at remote site using artificial intelligence enabled imaging modality, according to one embodiment;

[0029] FIG. 2A is a block diagram of the artificial intelligence enabled imaging modality for performing medical imaging procedure on an individual at a remote site, according to one embodiment;

[0030] FIG. 2B is a diagrammatic representation of a two wheeled vehicle for transporting the artificial intelligence enabled imaging modality, a printer, and a battery pack, according to one embodiment.

[0031] FIG. 3 is a block diagram of a processing unit deployed in the artificial intelligence enabled imaging modality, according to one embodiment;

[0032] FIG. 4 is a process flowchart depicting a method of determining structural deformity / infection in an individual using a trained artificial intelligence model, according to one embodiment; and

[0033] FIG. 5 is a block diagram depicting a cloud computing system with a cloud computing platform connectable to the artificial intelligence enabled imaging modality via a network, according to one embodiment.DETAILED DESCRIPTION

[0034] An artificial intelligence based portable X-ray imaging modality and method thereof is disclosed. Various embodiments are described with reference to the drawings, wherein like reference numerals are used to refer the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide thorough understanding of one or more embodiments. It may be evident that such embodiments may be practiced without these specific details.

[0035] The terms ‘radiological diagnostic system’, ‘X-ray imaging modality’, ‘imaging modality’ means the same and are used interchangeably throughout the document. The term ‘structural deformity / infection’ refers to damage caused to a bone / joint, ligament injury, tendon injury, muscle injury including but not limited to bone fracture, ligament / tendon tears, dislocation of joints and infection caused to organs like lungs. The terms ‘radiological diagnostics’ and ‘medical imaging procedure’ means the same and are used interchangeably throughout the document.

[0036] FIG. 1 is a schematic diagram depicting a system 100 for detecting fracture in body of an individual at remote site using artificial intelligence enabled imaging modality, according to an embodiment of the present invention. The system 100 includes a portable X- ray imaging modality 102, a cloud computing platform 104, and a network 106. The portable X-ray imaging modality is communicatively coupled to the cloud computing network 104 via the network 106.

[0037] The portable X-ray imaging modality 102 is an imaging modality which can be carried to a remote site such as accident site or home of a bedridden patient, etc. to determinepresence of structural deformity / infection in a body part of an individual using X-ray beam. The portable X-ray imaging modality 102 is capable self-configuring configuration parameters based on target body part (e.g., part of body / bone) on which medical imaging procedure is to be performed. In an embodiment, the imaging modality 102 is capable of automatically determining configuration parameter values based on part of body of an individual. For example, configuration values may include voltage and current required to generate X-ray beam of specific energy and wavelength. The imaging modality 102 is capable of generating X-ray beam of specific energy and wavelength focusing on the target body part and generating a high-quality digital radiograph of the target body part based on X-rays detected by an X-ray detector. The imaging modality 102 is capable of determining structural deformity / infection in the target body part of the individual using a primary trained artificial intelligence model based on the high-quality digital radiograph. The imaging modality 102 is configured to display the high-quality digital radiograph indicating presence of structural deformity / infection in the target body part on an integrated display unit. The imaging modality 102 is configured to print the high-quality digital radiograph using a printer attached to the imaging modality.

[0038] The cloud computing platform 104 is configured to provide cloud-based services such as data storage, data analytics, and data visualization. For example, the data stored in the cloud computing platform 104 can be accessed using devices such as smartphone, laptop, tablet, etc using the Internet from any locations. For example, a radiologist can access data stored on the cloud computing platform 104 from a geographical location remote from location of the imaging modality 102 (e.g., accident site).

[0039] In one embodiment, the cloud computing platform 104 is configured to receive the high-quality digital radiograph from the portable X-ray imaging modality 102 via the network 106. The portable X-ray imaging modality 102 may send the high-quality digitalradiograph to the cloud computing platform 104 via a secure communication channel (e.g., HIPAA-compliant communication channel). The portable X-ray imaging modality 102 may transfer the high-quality digital radiograph to the cloud computing platform 104 if presence of structural deformity or infection in the body of the individual is detected. In this embodiment, the cloud computing platform 104 hosts a secondary trained artificial intelligence model capable of determining type of structural deformity or infection and severity of the structural deformity / infection based on the high-quality radiograph. The cloud computing platform 104 determines type of structural deformity / infection, and severity of structural deformity / infection using the secondary trained artificial intelligence model based on the high-quality radiograph.

[0040] The cloud computing platform 104 is configured to generate at least one medical treatment recommendation to the individual in real-time based on the severity and type of structural deformity / infection associated with the individual. For example, if the individual has suffered a major fracture in the bone, the cloud computing platform 104 may classify the structural deformity as severe and type as major fracture and generate a medical treatment recommendation indicating that the individual needs to be taken to multi-speciality hospital which can perform surgery or cast. Alternatively, if the individual has suffered a minor fracture in the bone, the cloud computing platform 104 may classify the structural deformity as mild and type as minor fracture, and generate a medical treatment recommendation indicating that the individual can be taken to nearby clinic or health centre for treatment. The cloud computing platform 104 may communicate the severity, type of structural deformity, and medical treatment recommendation to the imaging modality 102 via the secure communication channel. The imaging modality 102 is configured to display the severity, type of structural deformity and the medical treatment recommendation on the integrated display unit. Advantageously, the system 100 enables to perform on-the-fly diagnosis of an individual esp. in emergency situations like accident. Thus, the individuals can be treated faster based on the severity andtype of structural deformity / infection. Also, hospital space or X-ray room can be better managed. Further, radiologist can advise by accessing the data of the individual in the cloud computing platform 104. The individuals will have less waiting time as the high-quality digital radiograph is ready before medical advice and treatment.

[0041] Additionally, the system 100 includes a printer 108 connected to the portable X- ray imaging modality 102 for printing the radiograph of the individual at the remote site. Also, the system 100 optionally includes a battery pack 110 coupled to the portable X-ray imaging modality for providing additional power supply to the portable X-ray imaging modality 102 and the printer 108. The imaging modality 102 may include onboard battery for supplying power to its components.

[0042] FIG. 2A is a block diagram of a portable X-ray imaging modality 102 for performing medical imaging procedure on an individual at remote site using artificial intelligence, according to an embodiment of the present invention. The portable X-ray imaging modality 102 includes an X-ray generator 202, an X-ray collimator 204, an X-ray detector 206, a processing unit 208, a display unit 212, and a sensor 214.

[0043] The X-ray generator 202 is configured to generate X-ray beam having energy range between 20keV to 120keV for performing a medical imaging procedure on an individual . The individual may be a patient who has met with an accident, official in war zone, person affected by natural calamity such as earthquake, floods, landslides or any other person who needs medical imaging procedure away from hospital / diagnostic lab. The X-ray generator 202 generates X-ray beam based on configuration parameter values such as voltage and current. The configuration parameter values are based on a target body part of the individual. Accordingly, the X-ray generator 202 may generate the X-ray beam with desired energy andwavelength for medical imaging of the target body part. An exemplary X-ray generator 208 used by the imaging modality 102 may be X-ray generator from Fujifilm® FDR, Delft or Mine.

[0044] The X-ray collimator 204 is configured to focus the X-ray beam 210 on the target body part. The X-ray collimator 204 includes a provision to receive a filter screen based on the target body part. For example, in case of soft X-ray, a filter screen suitable for soft X- ray (high energy and shortest wavelength) is fitted to the X-ray collimator 204. For hard X-ray, a filter screen suitable for hard X-ray (low energy and longest wavelength) is fitted to the X- ray collimator 204.

[0045] The X-ray detector 206 is configured to detect X-rays of the X-ray beam 210 that pass through the individual and generate representation. The X-ray detector 206 may be X-ray-sensitive plates or digital detector array configured to obtain details of the target body part on which medical imaging procedure is performed. For example, the X-ray detector 206 may be photostimulable phosphor plates (PSP), silicon devices such as charge -coupled devices (CCD), and complementary metal oxide semiconductors (CMOS).

[0046] The processing unit 208 is communicatively coupled to the X-ray generator 202, the X-ray collimator 204, the X-ray detector 206, the display unit 212, and the sensor 214. The processing unit 208 may be a graphics processor, embedded processor, vision processing unit FPGAs, multi-core CPUs and the like. The processing unit 208 is adapted to configure the X- ray generator 202 to generate X-ray beam of desired energy and wavelength using configuration parameters values. In one embodiment, the processing unit 208 may compute the configuration parameter values based on type of target body part. For example, if a target body part is chest, the processing unit 208 computes configuration parameter values required for medical imaging as 80 to 90 kVp and 5 to 16 mAs. However, if the target body part is lumbarspine, the processing unit 208 computes configuration parameter values as -100 kVp and -18.5 mAs.

[0047] The processing unit 208 is configured to construct a digital radiograph of the target body part in DICOM format from data sensed by the X-ray detector 206. The processing unit 208 transforms the digital radiograph into a high-quality digital radiograph of the target body part using image enhancement techniques. It can be noted that decreasing mAs can produce a high-quality digital radiograph with a lower radiation dose. Also, lower kVp produces a radiograph with high contrast. The high quality digital radiograph is obtained using optimal values of kVp and mAs and also appropriate filters.

[0048] The processing unit 208 determines whether the target body part is correctly captured in the high-quality digital radiograph. If the target body part is not correctly captured in the high-quality digital radiograph, the processing unit 208 computes configuration parameter values and reconfigures the X-ray generator 202 to generate an X-ray beam having modified energy and wavelength. The processing unit 208 determines presence of structural deformity / infection in the target body part using a trained artificial intelligence model based on the high-quality digital radiograph. The processing unit 208 is configured to send the high- quality radiograph to the cloud computing platform 104 if the presence of structural deformity / infection in the target body part is detected.

[0049] The display unit 212 may be a touchscreen-based display or static display having a graphical user interface. The display unit 212 is communicatively coupled with the processing unit 208. The display unit 212 enables an operator to adjust parameters associated with the imaging modality such as movement of the X-ray collimator 204 in order to focus the X-ray beam on the target body part. The display unit 212 displays information such as structural deformity / infection in the target body part, type and severity of structural deformity / infection,and recommendation for medical treatment based on the type and severity. The display unit 212 may enable to give a print command for printing the digital radiograph via the printer 108 connected to the portable X-ray imaging modality 102. The display unit 212 may display battery level associated with the battery pack 110.

[0050] The sensor 214 may be a camera or object detector which is communicatively coupled to the processing unit 208. The sensor 214 is configured to detect target body part on which medical imaging procedure is to be performed based on position of detector, position of the individual and so on. The sensor 214 communicates detected target body part to the processing unit 208 in the form of image or coordinates such that the processing unit 208 computes configuration parameter values for the X-ray generator 202.

[0051] FIG. 2B is a diagrammatic representation 250 of a two wheeled vehicle 254 for transporting the portable imaging modality 102, the printer 108, and the battery pack 110, according to an embodiment of the present invention. As shown in FIG. 2B, the vehicle 254 includes a carriage 252 mounted on a rear seat of the vehicle 254. The carriage 252 is a closed container in which the imaging modality 102, the printer 108 and the battery pack 110 can be stored and transported to a remote site (e.g., war zones, disaster zones, accident site, old age home, etc.) where radiological diagnostics is to be performed. Advantageously, the imaging modality is portable and can be easily transported unlike the conventional imaging modalities.

[0052] FIG. 3 is a block diagram of a processing unit 208 deployed in the portable X- ray imaging modality 102, according to an embodiment of the present invention. The processing unit 208 includes a configuration module 302, a radiograph generation module 304, a detection module 306, a communication module 308, an input / output module 310, and a print module 312.

[0053] The configuration module 302 is configured to dynamically determine type of target body part of an individual on which medical imaging procedure is to be performed and determine configuration parameter values based on type of target body part. For example, the type of target body is determined based on images of target body part captured by a camera. Alternatively, the type of target body part may be inputted by an operator of the portable X-ray imaging modality via set of keys or integrated touchscreen display unit. The configuration parameters may include voltage and current to be supplied to an X-ray generator to generate X-ray beam with specific energy and wavelength corresponding to the target body part.

[0054] The radiograph generation module 304 is configured to generate a standard quality radiograph of the target body part based on X-rays detected by X-ray detector that passed through the target body part. The radiograph generation module 304 is configured to transform the standard quality digital radiograph of the target body part into a high quality digital radiograph. For example, the radiograph generation module 304 may enhance density, contrast, definition and distortion of the digital radiograph to generate high quality digital radiograph.

[0055] The detection module 306 is configured to determine whether the target body part is correctly captured during medical imaging procedure. The detection module 306 is configured to pre-process the high-quality digital radiograph and detect presence of structural deformity / infection in the target body part using a trained artificial intelligence model based on the high-quality digital radiograph of the target body part. In some embodiments, the detection module 306 extracts features from the high quality digital radiograph and applies the extracted features to the trained artificial intelligence model. In these embodiments, the trained artificial intelligence model processes the extracted features and indicates whether the target body part is structurally deformed or has infection. The set of features may include region of interest in the high-quality digital radiograph of the target body part. The radiograph mayinclude bone portion which is identified and segmented into multiple bounding boxes. Each of these bounding boxes forms a region of interest for extracting features.

[0056] For example, a binary classification model (e.g., decision tree model) is trained using training data set (e.g., features of fractured bone and normal bone). Upon successful completion of the training, the binary classification model detects presence of fracture in a bone or not based on input data set (e.g., features extracted from a high-quality digital radiograph indicating a fractured bone). The binary classification model detects structural deformity / infection irrespective of type of structural deformity / infection.

[0057] The communication module 308 is configured to transmit the high-quality digital radiograph to a cloud computing platform 104 via a secure communication channel if presence of structural deformities / infection is detected. In some embodiments, the cloud computing platform may perform further analysis of the high-quality digital radiograph to determine type and severity of structural deformity / infection and generate a medical treatment recommendation based on the type and severity of the structural deformity / infection. The communication module 308 may be an interface which can connect to the cloud computing platform 104 via the Internet (e.g., Wi-Fi, Mobile Hotspot, etc.).

[0058] The input / output module 310 is configured to display the high-quality digital radiograph and associated patient information on an integrated display unit. The input / output module 310 is configured to receive input from an operator. For example, the input can be type of target body part on which medical imaging procedure is to be performed. The input / output module 310 may be combination of keypad and display unit or a touch -sensitive display which serves as an input and output interface. The print module 312 is configured to print the digital radiograph via a printer connected to the imaging modality 104.

[0059] FIG. 4 is a process flowchart 400 depicting a method of determining structural deformity / infection in an individual using a trained artificial intelligence model, according to an embodiment of the present invention. At step 402, a target body part of an individual on which medical imaging procedure is to be performed is dynamically detected by a camera. At step 404, configuration parameter values are determined to configure an X-ray generator based on the target body part. The configuration parameter values include current and voltage values corresponding to specific energy and wavelength of X-ray beam to be focused on the target body part.

[0060] At step 406, an X-ray generator is configured to generate an X-ray beam having specific energy and wavelength based on the configuration parameter values. At step 408, a high-quality digital radiograph of the target body part is generated based on the X-ray beam having the specific energy and wavelength focused on the target body part. In some embodiments, a digital radiograph of the target body part is generated based on the X-ray beam focused on the target body part. The digital radiograph is transformed into the high-quality digital radiograph of the target body part using an image processing technique. At step 412, it is determined whether the high-quality digital radiograph correctly captures the target body part on which medical imaging procedure is to be performed. If the target body part is not correctly captured in the high-quality radiograph, the process 300 performs step 402.

[0061] If the target body party is correctly captured in the high-quality digital radiograph, at step 414, it is determined whether there is structural deformity / infection in the target body part based on the high quality radiograph using a trained artificial intelligence model. In some embodiments, the high-quality digital radiograph of the target body part is pre- processed. For example, a set of features are extracted from the high-quality digital radiograph. The set of features may include region of interest in the high-quality digital radiograph of the target body part. The radiograph may include bone portion which is identified and segmentedinto multiple bounding boxes. Each of these bounding boxes forms a region of interest for extracting features. The pre-processed high quality digital radiograph data is applied to the trained artificial intelligence model. Presence of structural deformity / infection to the target body part is determined using the trained artificial intelligence model in real time by processing the pre-processed radiograph data.

[0062] If there is structural deformity / infection to the target body party, at step 416, the high-quality digital radiograph and patient data is communicated to a cloud computing platform associated with an hospital / medical service provider via a secure communication channel. In some embodiments, the cloud computing platform determines type and severity of structural deformity / infection using a trained artificial intelligence model based on the high-quality digital radiograph of the individual. If there is no structural deformity / infection found, the process 300 is terminated at step 418.

[0063] FIG. 5 is a block diagram depicting a cloud computing system 500 with a cloud computing platform 104 connectable to the portable X-ray imaging modality via the network 106, according to an embodiment of the present invention. The cloud computing system 500 includes a cloud communication interface 502, a cloud computing hardware and OS 504, and a cloud computing platform 104. The cloud computing system 500 may be a public cloud computing system, a private cloud computing system, hybrid cloud computing system or community cloud computing system.

[0064] The cloud communication interface 502 may enable communication between cloud computing platform 104 and the portable X-ray imaging modality 102 via the network 106. The cloud computing hardware and OS 504 may include one or more servers on which an operating system is installed and including one or more processors, one or more storage devices for storing data, and other peripherals required for providing cloud computing functionality.The cloud computing platform 104 may be a platform which is capable of delivering functionalities such as data storage, data analysis, data visualization, data communication using cloud computing hardware & OS 504 via application programming interfaces (APIs) and algorithms, and capable of determining type of structural deformity / infection in a target body part, severity of the structural deformity / infection in the target body part, and generating medical treatment recommendations based on the type and severity of the structural deformity / infection.

[0065] The cloud computing platform 104 may include a structural deformity detection module 506, a severity detection module 508, a recommendation module 510. The structural deformity detection module 506 is configured to determine type of structural deformity / infection in a target body part of an individual using a trained artificial intelligence model based on the high-quality digital radiograph received from the portable X-ray imaging modality 102. In one embodiment, the high-quality digital radiograph is pre-processed by extracting a set of features from the high-quality digital radiograph. The set of features are extracted by identifying regions of interest from the digital radiograph. The regions of interest may be portions which represent the target body part in the digital radiograph. The extracted features are applied to the trained artificial intelligence model.

[0066] The trained artificial intelligence model processes the extracted features and determines type of structural deformity / infection present in the target body part. In some embodiments, the trained artificial intelligence model may be a classification model which is trained to determine one of possible types of structural deformity / infections in target body part using a training data set (e.g., features corresponding to different types of structural deformity / infection). For example, the trained classification model may be an artificial neural network (ANN) model which is trained to correctly classify input data set into one of several types of structural deformity / infection. For instance, a trained ANN model may classify thestructural deformity in the target body part into one of hairline fracture, closed fracture, open fracture, greenstick fracture, spiral fracture, oblique fracture, oblique fracture, compression fracture, comminuted fracture, avulsion fracture, segmental fracture and so on based on input data (e.g., features extracted from a high-quality digital radiograph). It can be noted that the structural deformity detection module 506 is capable of identifying different types of fracture in case multiple fracture in the target body part.

[0067] The severity detection module 508 is configured to assign a severity associated with the structural deformity / infection based on the type of structural deformity / infection. For instance, if the type of fracture is falling into category of major fracture, the severity detection module 508 assigns the severity as high risk. Alternatively, if the type of fracture is falling into the category of minor fracture, the severity detection module 508 assigns the severity as moderate risk. The recommendation module 510 is configured to generate one or more medical treatment recommendations based on the type and severity of the structural deformity / infection associated with the target body part. For example, if the type of fracture is determined as open fracture wherein the bone breaks through the skin and the severity is assigned as high risk as this type of fracture is dangerous, the recommendation module 510 may generate a recommendation indicating that the individual shall be immediately taken to a multi-speciality hospital for surgery. The recommendation module 510 is configured to communicate the recommendations along with type and severity to the portable X-ray imaging modality 102 such that the recommendation(s), type and severity are displayed on the integrated display unit. Advantageously, the individual can be treated without much delay and need not go through cumbersome process of medical diagnosis, and pain.

[0068] Further, the cloud computing system 500 as illustrated in FIG. 5 is shown purely for purposes of illustration and is not intended to be in any way inclusive or limiting to theembodiments that are described herein. For example, a typical cloud computing environment would include many more remote servers (e.g., physical host computing systems), which may be distributed over multiple data centers, which might include many other types of devices, such as switches, power supplies, cooling systems, environmental controls, and the like, which are not illustrated herein. It will be apparent to one of ordinary skill in the art that the example shown in FIG. 5, as well as all other figures in this disclosure have been simplified for ease of understanding and are not intended to be exhaustive or limiting to the scope of the idea.

[0069] In various embodiments, the imaging modality described above has an off-grid, on-site, portable radiological diagnostic and reporting system with primary supportive treatment capabilities. Also, the imaging modality has a compact and lightweight design that is ergonomic for carrying during disaster relief situations or in remote areas where access to diagnostic tools is limited. Additionally, the imaging modality has end-to-end provision for X- ray image production, transmission, storage, classification and ease of image enhancement using artificial intelligence that would significantly aid in fast and accurate diagnosis. This is particularly important in case of low velocity injuries for primary treatment at the place of need. Such provisions can timely bridge the gap of inadequate availability of expert medical care in remote regions during medical emergencies.

[0070] The present description has been shown and described with reference to the foregoing examples. It is understood, however, that other forms, details, and examples can be made without departing from the spirit and scope of the present subject matter that is defined in the following claims.

Claims

AMENDED CLAIMS received by the International Bureau on 11 July 2025(11.07.2025)What is claimed is:

1. A portable X-ray imaging modality (102) comprising: an X-ray generator (202) configured to generate an X-ray beam; an X-ray collimator (204) is configured to focus the X-ray beam on a target body part of an individual; an X-ray detector (206) is configured to detect the X-rays passing through the target body part; and a sensor (214) configured to detect the target body part of an individual on which medical imaging procedure is to be performed; characterized by: a processing unit (208) configured to: receive an image of the target body part of the individual from the sensor (214); determine the target body part on which the medical imaging procedure is to be performed using the received image of the target body part; compute the configuration parameter values for generating x-ray beam of desired energy and wavelength suitable for the determined target body part, wherein the configuration parameter values are computed such that the generated X-ray beam have energy range in the range of 20keV to 120keV and configure the X-ray generator (202) to generate the X-ray beam having desired energy and wavelength based on the computed configuration parameter values in such a manner that a high-quality digital radiograph of the determined target body part is generated;2. The imaging modality (102) of claim 1, wherein the X-ray collimator (204) is adapted to receive a filter screen to focus the X-ray beam on the target body part.

3. The imaging modality (102) of claim 1, wherein the processing unit (208) is configured to: generate a digital radiograph of the target body part based on the detected X-rays; and transform the digital radiograph into the high-quality digital radiograph of the target body part.

4. The imaging modality (102) of claim 1, wherein the processing unit (208) is configured to: determine whether the high-quality digital radiograph captures the target body part on which the medical imaging procedure is to be performed; and compute new configuration parameter values and reconfigure the X-ray generator (202) to generate an X-ray beam having modified energy and wavelength if it is determined that the target body part is not correctly captured in the high-quality digital radiograph.

5. The imaging modality (102) of claim 1, wherein the processing unit (208) is configured to: pre-process the high-quality digital radiograph of the target body part; apply the pre-processed high quality digital radiograph data to the trained artificial intelligence model; and determine presence of structural deformity / infection in the target body part using the trained artificial intelligence model.

6. The imaging modality (102) of claim 1, further comprising a communication module configured to: communicate the high-quality digital radiograph and patient data to a cloud computing platform (104) associated with an hospital via the network.

7. The imaging modality (102) of claim 1, further comprising: a display unit (212) configured for displaying the high-quality digital radiograph of the target body part.

8. The imaging modality (102) of claim 1, further comprising: a rechargeable battery pack (110) configured to supply power to the X-ray generator (202).

9. A method of detecting structural deformity / infection in patient body using a portable X-ray imaging modality (102), comprising: receiving, using a processing unit (208), an image of a target body part of an individual on which a medical imaging procedure is to be performed from the sensor (214); dynamically detecting, using a processing unit (208), a target body part on which medical imaging procedure is to be performed using the received image of the target body part; determining, using the processing unit (208), configuration parameter values to generate an x-ray beam of specific energy and wavelength suitable for the determined targetbody part, wherein the configuration parameter values are determined such that the generated X-ray beam has energy in the range of 20keV to 120keV; configuring, using the processing unit (208), an X-ray generator (202) to generate the X-ray beam having specific energy and wavelength based on the determined configuration parameter values; and generating, using the processing unit (208), a high-quality digital radiograph of the target body part in response to the X-ray beam having the specific energy and wavelength.

10. The method of claim 9, wherein the configuration parameter values comprise current and voltage values corresponding to the specific energy and wavelength of X-ray beam to be focused on the target body part.

11. The method of claim 9, further comprising detecting presence of structural deformity / infection in the target body part in real-time based on the high-quality radiograph using a trained artificial intelligence model.

12. The method of claim 9, wherein generating the high-quality digital radiograph of the target body part based on the X-ray beam having the specific energy and wavelength focused on the target body part comprises: generating a digital radiograph of the target body part based on the X-ray beam focused on the target body part; and transforming the digital radiograph into the high-quality digital radiograph of the target body part.

13. The method of claim 9, further comprising: determining whether the high-quality digital radiograph captures the target body part on which medical imaging procedure is to be performed; and computing new configuration parameter values and reconfiguring the X-ray generator (202) to generate an X-ray beam having modified energy and wavelength if it is determined that the target body part is not correctly captured in the high-quality digital radiograph14. The method of claim 9, wherein detecting presence of structural deformity / infection in the target body part in real-time based on the high-quality radiograph using the trained artificial intelligence model comprises: pre-processing the high-quality digital radiograph of the target body part;applying the pre-processed high quality digital radiograph data to the trained artificial intelligence model; and determining presence of structural deformity / infection in the target body part using the trained artificial intelligence model.

15. The method of claim 9, further comprising: communicating the high-quality digital radiograph and patient data to a cloud computing platform (104) associated with an hospital via the network.

16. A system (100) comprising: at least one portable X-ray imaging apparatus (102) configured to: detect a target body part of an individual on which medical imaging procedure is to be performed; receive an image of the target body part of the individual; determine the target body part on which the medical imaging procedure is to be performed using the received image of the target body part; determine configuration parameter values to configure an X-ray generator (202) for generating x-ray beam of desired energy and wavelength suitable for the determined target body part wherein, the configuration parameter values are determined such that the generated X-ray beam have energy range in the range of 20keV to 120keV; configure an X-ray generator (202) to generate an X-ray beam having a specific energy and wavelength based on the determined configuration parameter values; generate a high-quality digital radiograph of the detected target body part based on the X-ray beam having specific energy and wavelength; determine presence of structural deformity / infection in the target body part using a primary trained artificial intelligence model; and a cloud computing platform (104) communicatively coupled to the portable X-ray imaging modality (102), wherein the cloud computing platform (104) is configured to: receive the high-quality digital radiograph of the target body part and patient data from the portable X-ray imaging modality (102); and detect type and severity of structural deformity / infection present in the target body part using a secondary trained artificial intelligence model based on the high- quality digital radiograph.

17. The system (100) of claim 16, wherein the portable X-ray imaging modality (102) is configured to: pre-process the high-quality digital radiograph of the target body part; apply the pre-processed high quality digital radiograph data to the primary trained artificial intelligence model; and determine presence of structural deformity / infection in the target body part using the primary trained artificial intelligence model.

18. The system (100) of claim 16, wherein the portable X-ray imaging modality (102) is configured to: pre-process the high-quality digital radiograph of the target body part; apply the pre-processed high quality digital radiograph data to the secondary trained artificial intelligence model; and classify structural deformity / infection in the target body part using the secondary trained artificial intelligence model in terms of type of structural deformity / infection and severity.

19. The system (100) of claim 16, wherein the portable X-ray imaging modality (102) is configured to: determine whether there exists the structural deformity / infection in the target body part using the primary trained artificial intelligence model based on the high-quality radiograph; and if there exists the structural deformity / infection in the target body part, transmit the high-quality radiograph and patient data to the cloud computing platform (104).

20. The system (100) of claim 16, wherein the portable X-ray imaging modality (102) is configured to: determine whether the high-quality digital radiograph captures the target body part on which medical imaging procedure is to be performed; and compute new configuration parameter values and reconfigure the X-ray generator 202 to generate an X-ray beam having modified energy and wavelength if it is determined that the target body part is not correctly captured in the high-quality digital radiograph.

21. The system of claim 16, wherein the cloud computing platform (104) is configured to:generate at least one medical treatment for a patient based on the severity and type of structural deformity / infection.

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