Description:FIELD OF THE INVENTION
The present invention relates to the field of medical diagnostics and artificial intelligence. More particularly, it concerns a multi-modal deep learning system and method for precision diagnosis and prognosis in biomedical imaging. The invention integrates diverse biomedical data modalities such as imaging, genetic profiles, and electronic health records to enhance disease detection accuracy, automate diagnostic workflows, and improve patient outcome predictions.
BACKGROUND OF THE INVENTION
Medical imaging plays a crucial role in disease diagnosis, but manual analysis is time-consuming, subjective, and prone to errors. Recent advances in deep learning have shown great promise in automating image analysis. However, current models often rely on single data modalities, limiting their effectiveness. This project aims to develop a multi-modal deep learning framework that integrates diverse biomedical data (e.g., images, genetic profiles, electronic health records) to improve diagnostic accuracy and outcome prediction.
Focus: Leveraging multi-modal deep learning models to improve diagnostic accuracy, automate disease detection, and predict patient outcomes across various medical conditions (e.g., cancer, neurological disorders).
US20080109185: According to the present invention, the most challenging issues in this work have been to find systematic ways of enabling maintenance engineers to decide an adequate time for the replacement of vacuum pumps on the basis of their current performance assessment result. Further, the comparison of the currently evaluated diagnostics analysis results and the initial (or reference) data set is shown to enable maintenance engineers to decide the replacement of the considered vacuum pump according to the evaluated pump performance indicators. This quantitative diagnostic analysis result is expected not only to enable maintenance engineers to decide an adequate time for the replacement of vacuum pumps on the basis of their current performance assessment results but also to improve the reliability and confidence of the predictive maintenance of low vacuum pumps.\
US20100248985: Methods and apparatuses for selecting and arranging clinically relevant chromosomal loci allow an exemplary diagnostic array to simultaneously test for numerous genetic alterations that occur in many different parts of the human genome. Clinically irrelevant or ineffective loci are eliminated. One implementation increases reliability and accuracy by dividing the base-pair sequence of each chromosomal locus into segments and then assigning nucleic acid clones for comparative genomic hybridization to each different segment. The segments may overlap for increased resolution and control. Clones representing segments that are adjacent on a native chromosome are placed in non-adjacent target areas of the array to avoid interfering hybridization reactions. Arrangement motifs within an array may be redundantly repeated for high availability and increased reliability and accuracy of results. Techniques, hardware, software, logic engines, loci collections, and diagnostic arrays are described.
Medical imaging forms the backbone of disease detection and management but relies heavily on manual interpretation, which is time-consuming, subjective, and error-prone. While deep learning models have shown significant promise in medical imaging tasks such as tumor detection and organ segmentation, most current systems focus on single modalities such as imaging alone. This limitation prevents them from capturing the full clinical context needed for precise diagnosis. Furthermore, existing AI systems often act as “black boxes” with limited interpretability, leading to clinician hesitation in relying on predictions. Generalization across diverse hospitals and patient populations also remains a challenge, as many models show strong performance only on narrow datasets.
The present invention addresses these shortcomings by proposing a multi-modal deep learning framework that integrates data from multiple sources, including imaging, clinical history, and genetic data. It incorporates explainability techniques to provide interpretable outputs and is designed for robust generalization across datasets. This approach ensures higher diagnostic accuracy, more reliable prognostic predictions, and greater clinician trust in AI-assisted medical decision-making.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention provides a comprehensive deep learning framework for automated diagnosis and prognosis in biomedical imaging. The system integrates multiple data modalities into a unified deep learning pipeline, combining medical imaging such as MRI, CT, and histopathology with genetic data and electronic health records. This integration allows for a holistic understanding of disease progression and individualized patient care.
The architecture comprises a preprocessing module to clean and normalize multimodal data, a feature extraction module employing convolutional neural networks for imaging and embedding methods for textual or genetic data, and a multi-modal fusion module that integrates heterogeneous features. A predictive engine built with deep learning models classifies diseases, segments abnormalities, and forecasts patient outcomes.
A key aspect of the invention is the incorporation of explainability mechanisms such as SHAP values and Grad-CAM visualizations, which highlight critical regions or features influencing predictions. These interpretable outputs support clinical adoption by offering transparency.
The system is scalable, capable of handling large datasets and adaptable across healthcare environments. It addresses challenges of missing data, computational complexity, and generalization. By unifying multiple data modalities and providing explainable predictions, the invention significantly advances AI-enabled biomedical diagnosis and prognosis.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention discloses a multi-modal deep learning system designed to enhance the precision of diagnosis and prognosis in biomedical imaging. The system begins with a data collection and preprocessing module that gathers information from multiple sources, including magnetic resonance imaging, computed tomography scans, histopathology slides, genetic profiles, and electronic health records. This module applies normalization and standardization to harmonize diverse formats, and uses imputation methods to handle missing data, ensuring consistent quality across datasets.
Feature extraction is performed differently for each modality. Convolutional neural networks are applied to imaging data to capture hierarchical features that represent spatial and structural characteristics. Genetic and clinical records are processed using embedding layers or recurrent architectures to model sequential and categorical information. The outputs from these modality-specific extractors are aligned and passed into a fusion module.
The fusion module integrates multimodal features into a common latent representation, enabling the system to learn cross-modal relationships that single-modality systems cannot capture. This integration provides a comprehensive view of patient health by combining structural abnormalities visible in imaging with genetic predispositions and clinical history.
The predictive engine built on this fused representation applies deep neural networks to perform classification, segmentation, and prognostic prediction. For example, the system can detect tumors, delineate their boundaries, and predict recurrence risks or survival outcomes. By modeling both spatial and temporal dependencies across modalities, the system achieves higher accuracy and robustness than existing solutions.
To improve transparency, the invention incorporates explainable AI techniques. Gradient-weighted class activation mapping (Grad-CAM) highlights relevant regions in medical images, while SHAP values indicate the importance of features in genetic and clinical data. This interpretability provides clinicians with confidence in the system’s predictions and facilitates integration into real-world workflows.
The system is designed to generalize across varied healthcare settings. Training employs large-scale, diverse datasets with augmentation strategies to improve robustness. Validation across multiple sites ensures adaptability across hospitals with differing equipment and patient demographics. This generalization capability addresses one of the key limitations of current AI solutions in healthcare.
Scalability is achieved through modular design. Each data modality has its own preprocessing and feature extraction pipeline, which can be expanded or modified as new modalities become available. The fusion and prediction components operate flexibly, adapting to the available data even when certain modalities are missing.
The invention also supports federated learning configurations, enabling decentralized training without sharing raw data. This feature preserves privacy while allowing hospitals to collaboratively build powerful models. By combining interpretability, multimodality, scalability, and privacy-preserving mechanisms, the invention provides a transformative tool for biomedical diagnosis and prognosis.
Deep learning has emerged as a powerful tool in biomedical imaging, outperforming traditional machine learning and statistical models in tasks such as tumor detection, organ segmentation, and disease classification. Convolutional Neural Networks (CNNs), for instance, have achieved state-of-the-art performance in image recognition tasks by learning hierarchical features from data without the need for manual feature extraction.
In recent years, several deep learning models have been developed for various medical imaging modalities:
MRI (Magnetic Resonance Imaging): Used in neurology and oncology for detecting brain tumors and lesions.
CT (Computed Tomography) scans: Widely applied in lung cancer detection and COVID-19 diagnosis.
Histopathology images: Critical for cancer diagnosis by examining tissue samples
Deep Learning in Biomedical Imaging
Deep learning has emerged as a powerful tool in biomedical imaging, outperforming traditional machine learning and statistical models in tasks such as tumor detection, organ.
Best Method of Working
The best method of working involves training the system on a large multimodal dataset comprising imaging, genetic, and clinical records. Imaging features are extracted using CNN architectures, while clinical and genetic data are encoded through embedding and recurrent models. The fusion module integrates the features into a unified representation, which is then processed by a predictive engine for diagnosis and prognosis. During inference, new patient data is passed through the same pipeline, producing disease classification, segmentation maps, and outcome predictions. Explainable AI methods generate interpretability outputs for clinicians. This embodiment is the most effective, as it ensures robust performance across diverse modalities and provides real-time, interpretable results suitable for clinical deployment.
 
, Claims:1. A system for precision diagnosis and prognosis in biomedical imaging using deep learning, comprising:
a preprocessing module configured to clean, normalize, and format multimodal data including imaging, genetic profiles, and electronic health records;
a feature extraction module comprising convolutional neural networks for imaging and embedding mechanisms for genetic and clinical data;
a fusion module configured to integrate features from multiple modalities into a unified representation;
a predictive engine employing deep learning models to classify diseases, segment abnormalities, and forecast patient outcomes;
an explainability module providing interpretable outputs including visual and feature-based justifications; and
an output interface configured to deliver diagnostic results and prognostic predictions to clinicians.
2. The system as claimed in claim 1, wherein the preprocessing module applies normalization, standardization, and imputation for missing values.
3. The system as claimed in claim 1, wherein the feature extraction module employs recurrent architectures to process sequential clinical records.
4. The system as claimed in claim 1, wherein the fusion module generates a latent multimodal representation for integrated learning.
5. The system as claimed in claim 1, wherein the predictive engine simultaneously performs disease detection, segmentation, and outcome prediction.
6. The system as claimed in claim 1, wherein the explainability module employs SHAP values and Grad-CAM visualizations to enhance interpretability.
7. The system as claimed in claim 1, wherein the output interface highlights image regions or features influencing predictions for clinician review.
8. The system as claimed in claim 1, wherein the system is scalable to large datasets and generalizes across different hospitals and populations.
9. The system as claimed in claim 1, wherein the framework supports federated learning to enable collaborative training without raw data exchange.
10. A method for precision diagnosis and prognosis in biomedical imaging using deep learning, comprising:
collecting multimodal patient data including imaging, genetic, and clinical records;
preprocessing the data through normalization, standardization, and imputation;
extracting features using convolutional and embedding-based models;
integrating the features into a unified multimodal representation using a fusion module;
predicting diseases, segmentations, and outcomes using a deep learning predictive engine; and
generating interpretable outputs for clinician decision-making through explainable AI techniques.