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US-12620477-B2 - System and method for detecting lung abnormalities in a medical image

US12620477B2US 12620477 B2US12620477 B2US 12620477B2US-12620477-B2

Abstract

The present subject matter relates to a system ( 100 ) and a method ( 300 ) for detecting lung abnormalities in a medical image ( 101 ). The clinical decision support system designed to revolutionize medical diagnosis and treatment recommendations. The system ( 100 ) employs a multi-faceted approach, combining data collection, intelligence-based imaging model ( 207 ), and language model ( 208 ), each playing a pivotal role in enhancing clinical decision-making. Data collection forms the foundation, aggregating diverse data sources including chest images, patient history records, EHS, clinical research data and updated healthcare guidelines. This dataset fuels the training and optimization of intelligent models, ensuring their accuracy and relevance. Integration of the intelligent imaging and language model outputs yields an overall confidence score. The system then furnishes a comprehensive output, encompassing diagnosis, confidence score, and tailored treatment recommendations, facilitating informed clinical decision-making. This innovative framework holds promise in augmenting medical practice, offering enhanced diagnostic precision and personalized treatment guidance.

Inventors

  • Bunty Kundnani
  • Sri Anusha Matta
  • Ayushi Mahendra

Assignees

  • QURE.AI TECHNOLOGIES PRIVATE LIMITED

Dates

Publication Date
20260505
Application Date
20241121
Priority Date
20231020

Claims (19)

  1. 1 . A system ( 100 ) for detecting lung abnormalities in a medical image ( 101 ), characterized in that, the system ( 100 ) comprises: a server ( 104 ) comprising: a memory ( 203 ); a processor ( 201 ) coupled with the memory ( 203 ), wherein the processor ( 201 ) is configured to execute programmed instructions stored in the memory ( 203 ) to: collect data corresponding to the medical image ( 101 ) and a patient clinical data ( 102 ); identify lung abnormalities based on the collected data, using a hybrid machine learning system carried out by the processor ( 201 ), wherein the hybrid machine learning system is configured to perform steps of: detecting lung abnormality by analysing the medical image ( 101 ) using an imaging model ( 207 ); calculating an image confidence score corresponding to the detected lung abnormality; extracting patient related information by analysing the patient clinical data ( 102 ) using a large language model (LLM) ( 208 ); calculating a patient confidence score corresponding to the extracted patient related information; generating an abnormality confidence score by combining the image confidence score and the patient confidence score; and generate an output, wherein the output corresponds to the lung abnormality with a comprehensive diagnosis and the abnormality confidence score.
  2. 2 . The system ( 100 ) as claimed in claim 1 , wherein the output corresponds to personalized treatment recommendation on patient medical history and clinical data.
  3. 3 . The system ( 100 ) as claimed in claim 1 , wherein the system ( 100 ) enables monitoring the lung abnormalities over time; wherein monitoring the lung abnormalities helps in tracking disease progression and adjustment of treatment plan.
  4. 4 . The system ( 100 ) as claimed in claim 1 , wherein the system ( 100 ) enables providing accurate and up-to-date lung abnormality diagnosis based on continuously learning from clinical evidence over time and adapting the system ( 100 ) accordingly.
  5. 5 . The system ( 100 ) as claimed in claim 1 , wherein the medical image ( 101 ) corresponds to chest image data in form of one of x-ray scans, CT scans, MRI scans, ultrasound images, and a combination thereof.
  6. 6 . The system ( 100 ) as claimed in claim 1 , wherein the patient clinical data ( 102 ) corresponds to one of patient history records, an electronic health records (EHR), clinical research data, updated medical device or healthcare guidelines available on public platforms and a combination thereof, wherein the clinical research data corresponds to research information published in domain of lung abnormalities.
  7. 7 . The system ( 100 ) as claimed in claim 1 , wherein the processor ( 201 ) of the server ( 104 ) is configured to train the imaging model ( 207 ) and the LLM ( 208 ) using the data collected by the processor ( 201 ) of the server ( 104 ).
  8. 8 . The system ( 100 ) as claimed in claim 1 , wherein the imaging model ( 207 ) corresponds to a deep learning algorithm, wherein lung abnormalities detected using the imaging model ( 207 ) corresponds to one of Asthma, Chronic obstructive pulmonary disease (COPD), Bronchiectasis, Bronchitis, pneumothorax, atelectasis, lung inflammation, Pulmonary fibrosis, Sarcoidosis, Lung cancer, Lung Infection (Pneumonia), Hyperinflation/Emphysema, Consolidation, Opacity, Scoliosis, Fibrosis, Tuberculosis screening, Atelectasis, Reticulo-nodular pattern, Nodules, Cavity, Calcification, Linear Opacities, Lung Nodule Malignancy, Covid-19 risk and a combination thereof.
  9. 9 . The system ( 100 ) as claimed in claim 1 , wherein the imaging model ( 207 ) is configured to calculate the image confidence score based on the analysis of the medical image ( 101 ).
  10. 10 . The system ( 100 ) as claimed in claim 1 , wherein the large language model ( 208 ) corresponds to Generative Pre-trained Transformer (GPT) model, wherein the patient related information extracted by the LLM ( 208 ) corresponds to one of identified risk factors, underlying cause of lung disease and a combination thereof, wherein the LLM ( 208 ) is configured to extract the patient related information from an unstructured clinical text and to provide treatment options.
  11. 11 . A method ( 300 ) for detecting lung abnormalities in a medical image ( 101 ), characterized in that, the method ( 300 ) comprises: collecting ( 301 ) data, by a processor ( 201 ) of a server ( 104 ), wherein the data corresponds to the medical image ( 101 ) and a patient clinical data ( 102 ); analysing ( 302 ), by the processor ( 201 ) of the server ( 104 ) the collected data by using a hybrid machine learning system, wherein the hybrid machine learning system performs steps of: detecting ( 303 ) lung abnormality by analysing the medical image ( 101 ) using an imaging model ( 207 ); calculating ( 304 ) an image confidence score corresponding to the detected lung abnormality; extracting ( 305 ) patient related information by analysing the patient clinical data ( 102 ) using a large language model (LLM) ( 208 ); calculating ( 306 ) a patient confidence score corresponding to the extracted patient related information; generating ( 307 ) an abnormality confidence score by combining the image confidence score and the patient confidence score; and generating ( 308 ), by the processor ( 201 ) of the server ( 104 ) an output, wherein the output corresponds to the lung abnormality with a comprehensive diagnosis and the abnormality confidence score.
  12. 12 . The method ( 300 ) as claimed in claim 11 , wherein the medical image ( 101 ) corresponds to chest image data in form of one of x-ray scans, CT scans, MRI scans, ultrasound images, and a combination thereof.
  13. 13 . The method ( 300 ) as claimed in claim 11 , wherein the patient clinical data ( 102 ) corresponds to one of patient history records, an electronic health records (EHR), clinical research data, updated medical device or healthcare guidelines available on public platforms and a combination thereof, wherein the clinical research data corresponds to research information published in domain of lung abnormalities.
  14. 14 . The method ( 300 ) as claimed in claim 11 , wherein the method ( 300 ) comprises training the imaging model ( 207 ) and the LLM ( 208 ) using the data collected by the processor ( 201 ) of the server ( 104 ).
  15. 15 . The method ( 300 ) as claimed in claim 11 , wherein the imaging model ( 207 ) corresponds to a deep learning algorithm, wherein lung abnormalities detecting using the imaging model ( 207 ) corresponds to one of Asthma, Chronic obstructive pulmonary disease (COPD), Bronchiectasis, Bronchitis, pneumothorax, atelectasis, lung inflammation, Pulmonary fibrosis, Sarcoidosis, Lung cancer, Lung Infection (Pneumonia), Hyperinflation/Emphysema, Consolidation, Opacity, Scoliosis, Fibrosis, Tuberculosis screening, Atelectasis, Reticulo-nodular pattern, Nodules, Cavity, Calcification, Linear Opacities, Lung Nodule Malignancy, Covid-19 risk and a combination thereof.
  16. 16 . The method ( 300 ) as claimed in claim 11 , wherein the large language model ( 208 ) corresponds to Generative Pre-trained Transformer (GPT) model, wherein the patient related information extracted by the LLM ( 208 ) corresponds to one of identified risk factors, underlying cause of lung disease and a combination thereof, wherein the patient related information is extracted from an unstructured clinical text and a treatment option recommended by the LLM ( 208 ).
  17. 17 . The method ( 300 ) as claimed in claim 11 , wherein the output corresponds to personalized treatment recommendation on patient medical history and clinical data.
  18. 18 . The method ( 300 ) as claimed in claim 11 , wherein the method ( 300 ) enables monitoring the lung abnormalities over time; wherein monitoring the lung abnormalities helps in tracking disease progression and adjustment of treatment plan.
  19. 19 . The method ( 300 ) as claimed in claim 11 , wherein the method ( 300 ) enables providing accurate and up-to-date lung abnormality diagnosis based on continuously learning from clinical evidence over time and adapting the method ( 300 ) accordingly.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY The present application does claim priority from Indian Patent Application number 202321071661 filed on 20 Oct. 2023. FIELD OF INVENTION The present subject matter described herein, in general, relates to management of medical abnormalities. More specifically, the present invention relates to detection of lung abnormalities. More particularly, the present invention relates to artificial intelligence (AI) enabled clinical decision support system for detection and management of lung abnormalities on a medical image. BACKGROUND OF THE INVENTION This section is intended to introduce the reader to various aspects of art, which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology. Artificial intelligence has revolutionized the healthcare industry by enabling the analysis of patient data, whether in the form of text or medical images. AI-driven systems can swiftly and accurately interpret complex medical records and diagnostic images, aiding healthcare professionals in making more informed decisions. Specifically, the usage of imaging AI for scanning medical images can detect lung abnormalities which may either overlooked or difficult for human experts to identify. However, challenges persist in conventional systems. In text analysis, the sheer volume of unstructured patient data poses difficulties for manual processing, potentially leading to overlooked insights. In image analysis, AI models may require extensive and high-quality datasets for training, which can be scarce. Moreover, concerns about patient data privacy and model interpretability must be addressed to ensure ethical and secure AI integration into healthcare. Further, Personalization is a cornerstone of effective disease management. Each patient's medical history, genetics, lifestyle, and preferences play a pivotal role in determining the most suitable interventions. Empowering patients with personalized care plans encourage greater adherence to treatment regimens, leading to improved health care and reduced complications. Traditional healthcare decision support system lacks personalized disease management due to leveraging a single AI model (specifically Imaging AI) for analysing user's medical image. The usage of image-based AI model to identify medical abnormalities are solely based on real-time inputs from the medical image scanned by the imaging AI model. However medical history and current medical condition of the user may have an impact on the medical abnormalities which may or may not be captured in the observations made through scanning the medical image solely. Therefore, a need for analysing the medical history or reports along with user's medical image is expected to perform comprehensive analysis on the user data. Conventionally healthcare system utilizing the NLP (natural language processing) model has several drawbacks. It often struggles with understanding and interpreting nuanced medical language, leading to potential misinterpretations and errors in clinical documentation. NLP systems can be rigid, requiring constant updates to adapt to evolving medical terminology and language patterns. They may lack the contextual awareness and comprehension capabilities that large language models possess, limiting their ability to extract meaningful insights from complex medical texts. Additionally, building and maintaining NLP-based systems can be resource-intensive and time-consuming, making them less efficient and costly approach. Further, the absence of large language models (LLMs) such as GPT (generative pre-trained transformer) models in the analysis of patient medical health presents significant shortcomings in healthcare. GPT models, with their natural language understanding capabilities, could greatly enhance the interpretation of unstructured patient data, such as clinical notes and medical histories. Without their use, healthcare providers may struggle to extract valuable insights from these textual sources, potentially missing critical information that could influence patient diagnoses and treatment decisions. GPT models can also aid in automating tasks like medical coding and summarizing complex patient records, saving time and reducing human err