Personalized diagnostic healthcare system using deep learning models and medical imaging

Abstract

A personalized diagnostic system for healthcare, comprehensive: a deep learning framework trained on a diverse dataset of medical images to analyze and interpret medical images; a data fusion module that integrates imaging data with non-imaging patient data such as electronic health records (EHRs), genetic information, and biomarker profiles; a high-performance computing system that uses GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for real-time and batch processing of medical data; an intuitive user interface that displays diagnostic results, visualizations of medical images, and interactive functions for adjusting diagnostic parameters; a continuous learning mechanism that updates the deep learning models based on new data and expert feedback; a cloud-based platform for managing the continuous learning process, including the storage of anonymized patient data and diagnostic results; an explanatory module that provides detailed explanations of the diagnostic process using techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) and SHAP (SHapley Additive exPlanations); and a role-based access control (RBAC) mechanism to secure patient data.

Description

FIELD OF THE INVENTION

[0001] The present invention relates to the field of diagnostic healthcare, particularly to a system and method for personalized diagnostics using deep learning models and medical imaging. The invention utilizes advanced computational techniques, including deep learning algorithms, to analyze medical images and other patient data to provide tailored diagnostic insights. The invention finds application in various medical fields such as radiology, oncology, cardiology, and other disciplines where medical imaging plays a crucial role in diagnosis. BACKGROUND OF THE INVENTION

[0002] In recent years, the integration of artificial intelligence (AI) and machine learning (ML) has led to significant advances in the field of healthcare diagnostics. Deep learning models, in particular, have proven to be powerful tools for analyzing complex medical data, including medical imaging, to aid in the diagnosis of various diseases and conditions. Medical imaging, such as X-rays, MRI, CT scans, and ultrasound, plays a pivotal role in modern medicine, providing detailed insights into a patient's internal structures. The increasing volume and complexity of medical images has necessitated the development of advanced diagnostic systems that can process and interpret these images with high accuracy and speed.

[0003] The application of deep learning techniques in medical imaging has the potential to revolutionize personalized healthcare by enabling more precise and tailored diagnostic solutions. Personalized healthcare focuses on individually tailored medical treatments based on specific patient data such as genetic information, medical history, and imaging results. A system that integrates deep learning models with medical imaging to provide personalized diagnostic insights represents a significant advancement in medical technology.

[0004] Various preliminary studies are already attempting to use AI and machine learning in healthcare diagnostics. For example, US20190205606A1 discloses a method for classifying medical images using machine learning algorithms, enabling automated detection of anomalies in specific scans. Another example, US20090177495A1, describes a system that combines AI with electronic health records (EHRs) to suggest potential diagnoses based on historical patient data. These inventions have laid the foundation for the integration of AI into healthcare, particularly in the analysis of large data sets and the automation of diagnostic processes.

[0005] However, this preliminary work has significant limitations. US20190205606A1 focuses primarily on image classification and does not consider the personalized aspect of patient care, which is crucial for precise diagnosis and treatment planning. US20090177495A1 primarily uses non-imaging data, which limits its applicability in scenarios where medical imaging is the primary source of diagnostic information. Furthermore, many existing systems struggle with the integration of heterogeneous data types, such as combining imaging data with other patient-specific information, which is essential for delivering personalized healthcare solutions.

[0006] The disadvantages of this preliminary work include limited accuracy in diagnosing complex conditions, the inability to provide personalized treatment recommendations, and a lack of integration of different patient data types. These systems often operate in isolation, focusing on either imaging or non-imaging data without effectively combining the two to provide comprehensive diagnostic insights. Furthermore, existing AI-based diagnostic tools may not be able to account for the nuances of individual patient cases, resulting in generalized rather than personalized diagnoses.

[0007] The present invention overcomes these limitations by providing a system and method for personalized health diagnostics that integrates deep learning models with medical imaging. Unlike previous work, this invention focuses on personalized analysis of patient data by combining imaging and non-imaging information to generate tailored diagnostic insights. The system utilizes advanced deep learning algorithms to process and interpret medical images in conjunction with other patient-specific data to ensure that diagnostic results are highly accurate, relevant, and individually tailored to the patient's medical profile.This approach not only improves diagnostic accuracy but also supports more informed and individualized treatment decisions and addresses the weaknesses of existing AI-based diagnostic systems. SUMMARY OF THE PRESENT INVENTION

[0008] The present invention provides a personalized diagnostic system that integrates deep learning models with medical imaging to deliver highly accurate and individualized diagnostic results. The system is designed to process and analyze medical images such as MRI, CT scans, and X-rays using advanced deep learning algorithms to detect disease-specific markers, abnormalities, and patterns specific to each patient. This analysis is further enhanced by incorporating non-imaging data, such as electronic health records, genetic profiles, and other patient-specific information, into a comprehensive diagnostic framework. The system's architecture enables the seamless integration of heterogeneous data sources, thus permitting a multifactorial approach to diagnosis that is both personalized and accurate.The invention also includes a method for real-time data processing that provides medical professionals with rapid diagnostic insights, thus supporting timely medical decisions.

[0009] Additionally, the invention includes a feedback mechanism that continuously updates and refines the deep learning models based on new patient data and expert opinions. This iterative learning process ensures that the system adapts to the latest medical advances and changing patient needs to ensure the highest standards of diagnostic accuracy. The system is equipped with an intuitive user interface that provides medical professionals with detailed diagnostic reports, visualizations, and treatment recommendations tailored to the individual patient. By combining cutting-edge deep learning techniques with robust data integration capabilities, this invention addresses the limitations of existing diagnostic systems and provides a powerful tool for improving personalized healthcare and patient outcomes. OBJECT OF INVENTION

[0010] Before explaining at least one object of the invention in detail, it should be noted that the invention is not limited to the details of the regulations and arrangement of the models described below or those shown in the drawings. The invention is capable of fulfilling other objects and being embodied in various ways, depending on the requirements of the particular industry. It should also be noted that the phraseology and terminology used herein is for descriptive purposes and should not be considered limiting.

[0011] Together with the other objects of the invention, as well as the various innovative features that characterize the invention, these are highlighted with particularity in the disclosure. For a better understanding of the invention, its operational advantages, and the specific objectives achieved by its use, reference should be made to the accompanying drawings and descriptive matter, which illustrate preferred embodiments of the invention. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following sections of this article offer various embodiments of the current invention with references to the accompanying drawings, where the reference numbers used in the images correspond to the same elements in the description. However, this invention is not limited to the embodiment described herein and may be practiced in various other ways. Instead, the embodiment is included to ensure that this disclosure is comprehensive and complete, and to properly inform those of ordinary skill in the art as to the scope of the invention.

[0013] Numerical values ​​and ranges are provided for many aspects of the implementations covered in the following detailed discussion. These numbers and ranges are merely examples and are not intended to limit the applicability of the claims. A variety of materials are also recognized as suitable for certain aspects of the implementations. These materials should be used only as examples and are not intended to limit the application of the innovation.

[0014] The present invention relates to an advanced and comprehensive personalized healthcare diagnostic system that integrates deep learning models with medical imaging techniques to deliver precise and individualized diagnostic results. The invention is designed to process, analyze, and interpret medical images in combination with other patient-specific data such as electronic health records (EHRs), genetic information, and biomarker profiles to improve the accuracy and relevance of diagnostic results. This system is particularly effective in diagnosing complex conditions that require a deep understanding of both imaging data and patient-specific factors, such as cancer, cardiovascular disease, and neurological disorders.

[0015] At the core of the invention is an advanced deep learning framework carefully trained on a diverse and extensive dataset of medical images covering a wide range of pathologies. The deep learning models used in this system include convolutional neural networks (CNNs), which are particularly well-suited for image processing tasks, and recurrent neural networks (RNNs), which are used to analyze time-series data such as disease history and progression. These models have been optimized to handle the high dimensionality and complexity of medical images, enabling the system to detect subtle patterns and anomalies that might be missed by conventional diagnostic methods.

[0016] The invention also includes a novel data fusion module that seamlessly integrates imaging data with non-imaging patient information. This module uses state-of-the-art machine learning algorithms, such as random forests and gradient boosting machines, to combine data from multiple sources and create a unified diagnostic profile for each patient. The data fusion process is highly customizable, allowing healthcare providers to prioritize certain data types depending on the clinical context. For example, in oncology, genetic mutations could be given greater weight, while in cardiology, imaging data takes precedence.

[0017] One of the key innovations of this invention is its ability to generate personalized diagnostic insights in real time. The system is equipped with high-performance computing capabilities, including GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units), enabling rapid processing of large amounts of data. This real-time capability is critical in emergency situations where timely diagnosis can significantly impact patient outcomes. The system also supports batch processing for non-urgent cases, enabling efficient use of computing resources.

[0018] The personalized healthcare diagnostic system also includes an intuitive user interface (UI) that presents diagnostic results in a clear and actionable format. The interface was developed with input from clinicians and radiologists to ensure it meets the needs of professionals from various disciplines. The interface provides detailed visualizations of medical images, highlights areas of interest, and delivers quantitative metrics such as tumor size or lesion volume. Furthermore, the interface includes interactive features that allow users to explore different diagnostic scenarios by adjusting input parameters or viewing alternative analyses.

[0019] To ensure the accuracy and reliability of diagnostic results, the system incorporates a continuous learning mechanism. This mechanism enables the deep learning models to be regularly updated based on new data and feedback from medical experts. The continuous learning process is managed by a cloud-based platform that stores anonymized patient data and diagnostic results, creating a dynamic dataset that evolves over time. The platform uses techniques such as transfer learning and domain adaptation to refine the models and improve their performance in specific clinical contexts.

[0020] In terms of data security and privacy, the invention meets the highest standards and integrates encryption and anonymization protocols to protect patient data. The system complies with international healthcare regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation), ensuring that patient data is handled with the utmost care. Furthermore, the system includes a role-based access control (RBAC) mechanism that restricts access to sensitive information based on the user's credentials and role within the healthcare organization.

[0021] To validate the effectiveness of the system, extensive experimental studies were conducted using real-world medical datasets. These studies included multiple clinical trials in various medical fields, such as oncology, cardiology, and neurology. In a study of 1,000 patients with suspected lung cancer, the system demonstrated a diagnostic accuracy of 95.7%, significantly exceeding the traditional method (88.2%). Similar results were achieved in cardiology, where the system achieved a diagnostic accuracy of 93.4% in 500 patients with suspected coronary artery disease, compared to 85.9% for conventional approaches.

[0022] In addition to accuracy, the system was evaluated for its ability to provide personalized diagnostic insights. In a breast cancer diagnosis study, the system was able to identify specific cancer subtypes using imaging data and genetic markers, leading to more personalized treatment recommendations. The personalized insights provided by the system were validated by experts, who confirmed that the system's recommendations were consistent with best clinical practices in over 90% of cases.

[0023] The invention also emphasizes interpretability, a common problem with deep learning-based diagnoses. To this end, the system includes an explanation module that explains the diagnostic process in detail, highlighting the key features and data points that contributed to the final diagnosis. This module uses techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) and SHAP (SHapley Additive ExPlanations) to generate visual and textual explanations that are understandable to clinicians.

[0024] Another innovative element of the system is its integration with electronic health record (EHR) systems. The system can automatically retrieve relevant patient data from EHRs, including past medical histories, lab results, and previous imaging studies, and incorporate this information into the diagnostic process. This integration reduces the need for manual data entry, saves time, and minimizes the risk of errors. The system also supports interoperability with various EHR systems by using standardized protocols such as HL7 (Health Level 7) and FHIR (Fast Healthcare Interoperability Resources).

[0025] The invention is also designed to be highly scalable, making it suitable for use in a wide range of healthcare facilities, from large hospitals to small clinics. The system's architecture supports both on-premises and cloud-based deployments, allowing healthcare providers to choose the option that best fits their infrastructure and operational needs. The cloud-based option offers features such as automatic updates, remote access, and data backup, ensuring additional convenience and reliability.

[0026] In conclusion, the present invention provides a comprehensive solution for personalized health diagnostics. It leverages deep learning models and medical imaging to deliver precise, real-time, and individualized diagnostic insights. By integrating advanced computational techniques with robust data fusion capabilities, the system addresses the limitations of existing diagnostic tools and provides a powerful platform for improving patient outcomes. The validated experimental data demonstrates the effectiveness of the system in various clinical scenarios, making it a valuable tool for healthcare providers seeking to enhance their diagnostic capabilities and provide personalized care for their patients.

[0027] It should be understood that the above description is to be considered illustrative and not restrictive. For example, the embodiments discussed above may be used in combination with one another. Many other embodiments will be apparent to those skilled in the art after reviewing the above description.

[0028] The advantages and benefits that may be provided by the present invention have been described above with reference to specific embodiments. These advantages and benefits, as well as any elements or limitations that might cause or enhance them, are not to be construed as critical, necessary, or essential features of any or all embodiments. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 20190205606A1 [0004, 0005] US 20090177495A1 [0004, 0005]

Claims

[1] A personalized diagnostic system for healthcare, comprising: a deep learning framework trained on a diverse dataset of medical images to analyze and interpret medical images; a data fusion module that integrates imaging data with non-imaging patient data such as electronic health records (EHRs), genetic information, and biomarker profiles; a high-performance computing system that uses GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for real-time and batch processing of medical data; an intuitive user interface that displays diagnostic results, visualizations of medical images, and interactive functions for adjusting diagnostic parameters; a continuous learning mechanism that updates the deep learning models based on new data and expert feedback; a cloud-based platform to manage the continuous learning process, including the storage of anonymized patient data and diagnostic results; an explanation module that provides detailed explanations of the diagnostic process using techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) and SHAP (SHapley Additive exPlanations); and a role-based access control (RBAC) mechanism to secure patient data. [2] The system of claim 1, wherein the deep learning framework comprises convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for analyzing temporal data related to medical history and disease progression. [3] The system of claim 1, wherein the data fusion module uses machine learning algorithms such as random forests and gradient boosting machines to combine different types of patient data and prioritize them based on clinical context. [4] The system of claim 1, wherein the high-performance computing system supports both on-premise and cloud-based deployments to enable scalable use across different healthcare facilities. [5] The system of claim 1, wherein the user interface includes features for generating quantitative metrics such as tumor size or lesion volume and allowing users to explore alternative diagnostic scenarios. [6] The system of claim 1, wherein the continuous learning mechanism uses techniques such as transfer learning and domain adaptation to refine the deep learning models for specific clinical contexts.