System for improved breast cancer prognosis through multimodal machine learning

Abstract

A system for improved breast cancer prognosis using multimodal machine learning; the system includes: a central processing unit (CPU) configured to coordinate and execute data processing tasks; a multimodal data acquisition interface for communication with multiple diagnostic devices and hospital databases, wherein the multimodal data acquisition interface is configured to receive different datasets, including mammography images, histopathological images, genetic profiles and medical records, a feature extraction unit that is operationally connected to the central processing unit, wherein the feature extraction unit is configured to apply deep learning models to extract relevant features from the received datasets, including cell structures from histopathological images, masses or calcifications from mammographic images, and genetic mutations from genetic profiles; a machine learning engine integrated into the central processing unit and the feature extraction unit, wherein the machine learning engine is configured to analyze the extracted features using multimodal learning techniques, and wherein the machine learning engine is further configured to combine data from multiple modalities to generate a prognosis for breast cancer, the prognosis comprising an assessment of the disease course, the risk of relapse, and the likely treatment response; a forecast reporting unit in communication with the machine learning machine, wherein the forecast reporting unit is configured to generate a comprehensive forecast report in both visual and text formats, the report presenting multimodal analysis results in a format interpretable by medical professionals; and a power supply configured to continuously power the system, the power supply also including an uninterruptible power supply (UPS) to ensure uninterrupted operation of the system in the event of power outages or fluctuations.

Description

Field of the invention:

[0001] The present invention relates to the field of medical diagnostics and prognosis, more specifically to systems and methods that utilize artificial intelligence for breast cancer prognosis. The system integrates multimodal machine learning techniques to analyze various patient data such as histopathological images, medical records, genetic profiles, and mammograms to improve the accuracy of breast cancer prognosis. Background of the invention:

[0002] Breast cancer remains one of the most common cancers among women worldwide. Early diagnosis and accurate prognosis are crucial for effective treatment. Current diagnostic approaches often rely on data from single modalities such as mammograms or biopsies, limiting the scope and accuracy of prognostic predictions. Conventional systems also struggle with integrating large-scale multimodal data, which can lead to inefficient processing and lower accuracy. The need for an improved system that integrates multiple data types and improves diagnostic precision through machine learning is evident. This invention overcomes these limitations by using multimodal machine learning for improved breast cancer prognosis.

[0003] Breast cancer remains a significant global health problem, affecting millions of women each year. Early detection and accurate prognosis are critical for improving patient outcomes, as breast cancer can vary considerably in its aggressiveness, response to treatment, and likelihood of recurrence. Traditional diagnostic tools such as mammography, biopsy, and histopathological analysis have been widely used for decades to detect and analyze breast cancer. Although these methods play an essential role in the clinical management of the disease, they have significant limitations regarding prognostic accuracy and the ability to predict patient outcomes.Furthermore, the current technological landscape has evolved, with advances in artificial intelligence and machine learning offering new opportunities to improve breast cancer diagnosis and prognosis. Despite these innovations, however, existing solutions still face numerous challenges that hinder their full potential.

[0004] Mammography, one of the oldest and most widely used methods for breast cancer detection, uses low-dose X-rays to produce images of breast tissue. Although it remains the gold standard for early detection, mammography has well-documented drawbacks. False positive and false negative results are common, leading to unnecessary biopsies or missed cancers. Dense breast tissue, in particular, can make it difficult for mammograms to accurately detect tumors, resulting in lower sensitivity in certain populations. Furthermore, mammography focuses primarily on detection rather than prognosis. It provides limited information about tumor aggressiveness or the likelihood of recurrence, requiring additional diagnostic methods such as biopsies and genetic testing for more detailed prognostic assessments.

[0005] Histopathological analysis, in which tissue samples from a biopsy are examined under a microscope, is another important tool for diagnosing breast cancer. Pathologists assess the size, shape, and appearance of the cancer cells to determine the type and grade of the tumor. Although histopathology is a valuable diagnostic method, it relies heavily on human interpretation, introducing a degree of subjectivity into the process. Differences between pathologists can lead to inconsistencies in tumor classification and prognosis, impacting treatment decisions. Furthermore, histopathology provides static information at a single point in time and does not account for the dynamic and evolving nature of cancer.It also focuses on cell morphology, which may not always correlate with the underlying genetic and molecular features that drive cancer progression.

[0006] Genetic profiling has emerged as a complementary method to conventional diagnostic tools, offering deeper insights into the molecular mechanisms of breast cancer. Tests such as Oncotype DX and MammaPrint analyze the expression levels of specific genes associated with breast cancer recurrence and chemotherapy sensitivity. These tests have improved the ability to stratify patients based on their risk of recurrence and tailor treatments accordingly. However, genetic profiling remains limited in several respects. First, it typically focuses on a predefined set of genes that may not capture the full complexity of cancer biology. As a result, patients with similar genetic profiles may have different clinical outcomes.Furthermore, these tests are expensive and not widely available in all healthcare facilities, especially in low- and middle-income countries. Furthermore, genetic profiling, like histopathology, is based on static data from a biopsy, which may not fully reflect the dynamic nature of tumor evolution over time.

[0007] In recent years, artificial intelligence (AI) and machine learning (ML) have been used to diagnose and prognosticate breast cancer, offering the potential to overcome some of the limitations of traditional methods. AI systems can quickly process large amounts of data and detect patterns that may not be apparent to human clinicians. Machine learning techniques have been developed to analyze mammograms, biopsy images, and genetic data to aid in the detection and classification of breast cancer. For example, convolutional neural networks (CNNs), a type of deep learning technique, have been used to automatically detect cancerous lesions in medical images, reducing the burden on radiologists and improving diagnostic accuracy.

[0008] Most existing AI systems are designed to analyze a single modality, such as mammography images or genetic data, in isolation. This single-modality approach fails to capture the full complexity of breast cancer, which involves interactions between genetic, histological, and clinical factors. Without the integration of multiple data types, the prognostic predictions generated by these systems can be limited in their accuracy and generalizability.

[0009] Another limitation of current AI solutions is their reliance on high-quality, well-annotated datasets for training. Machine learning techniques require large amounts of labeled data to learn effectively. However, medical datasets, especially those containing diverse data types such as images, genetic profiles, and medical records, are often fragmented and difficult to obtain. Furthermore, these datasets can suffer from biases, such as overrepresentation of certain populations, which can impair the generalizability of AI models to broader patient populations.

[0010] Another significant drawback of current AI-based solutions is the "black box" nature of many machine learning techniques, particularly deep learning models. While these models can produce highly accurate predictions, they are often uninterpretable, making it difficult for clinicians to understand how the technology reached a particular decision. This lack of transparency can undermine trust in AI-based systems and limit their adoption in clinical practice. Both clinicians and patients may be reluctant to rely on AI predictions if they cannot be explained using clinically relevant factors.

[0011] In addition to these technical challenges, there are practical obstacles to implementing AI-based breast cancer prognostication systems in healthcare. Many existing solutions require specialized hardware, such as high-performance GPUs, which may not be available in all healthcare facilities, especially in resource-poor settings. Furthermore, integrating AI systems into existing clinical workflows can be challenging. Healthcare providers may need to invest in new infrastructure, train staff, and adapt their processes to enable AI-assisted diagnostics and prognostication.

[0012] Furthermore, regulatory hurdles must be overcome before AI-based systems can be widely used in clinical practice. Medical AI systems must undergo rigorous testing and validation to ensure their safety and effectiveness. Regulatory authorities such as the FDA in the US have begun to develop frameworks for evaluating AI-based medical devices, but the regulatory landscape is still evolving. AI systems that make autonomous diagnostic or prognostic decisions raise ethical and legal concerns, particularly regarding liability in the event of incorrect predictions.

[0013] And while AI has the potential to improve prognosis in breast cancer, it should be viewed as a complement, not a replacement, for human medical staff. The role of AI in healthcare should be to support physicians with additional insights and reduce diagnostic errors, not to replace the expertise and judgment of trained medical professionals. Collaboration between AI systems and physicians is essential to ensure the benefits of AI are utilized in a way that improves patient care and outcomes.

[0014] The limitations of existing solutions include their reliance on single-modality data, lack of integration, issues with dataset availability and quality, concerns about interpretability, and practical barriers to implementation. Summary of the invention:

[0015] The invention provides a system that integrates multimodal machine learning techniques to process and analyze diverse datasets, including, but not limited to, medical imaging (histopathology, mammography), clinical records, genetic profiles, and other diagnostic information. The system applies advanced feature extraction techniques to identify key biomarkers and patterns that correlate with breast cancer prognosis. It uses a machine learning-based classifier that dynamically adapts based on input data to provide highly accurate predictions regarding disease progression, treatment response, and patient outcomes. The system is intended to assist healthcare providers in making more informed decisions regarding breast cancer treatment plans.

[0016] The machine / structure device includes a central processing unit (CPU), a multimodal data acquisition interface, a feature extraction module, a machine learning engine, and a forecast reporting unit. The CPU coordinates the data flow between these units, ensuring efficient and timely analysis.

[0017] The objectives of the present invention are to provide a system that improves the accuracy of breast cancer prognosis through the integration of multimodal machine learning techniques. This invention aims to overcome the limitations of current single-modality diagnostic tools by combining various data types such as histopathological images, mammograms, genetic profiles, and clinical records into a unified prediction system. One of the main objectives is to improve the accuracy of prognostic predictions, allowing healthcare professionals to make more informed decisions regarding treatment strategies and thus improve patient outcomes.

[0018] Another goal of the invention is to reduce the subjectivity of traditional methods such as manual histopathological analysis by leveraging machine learning techniques that can automatically extract features and detect patterns in various data types. This automation aims to improve consistency in breast cancer diagnosis and prognosis, reduce interprofessional variability, and minimize errors.

[0019] Furthermore, the invention aims to address the challenge of integrating large medical data sets by providing a system capable of processing large amounts of data in real time. The goal is to enable faster and more efficient analyses, thus providing timely prognostic reports that can support immediate clinical decision-making. The system also aims to improve access to advanced diagnostic tools by providing a solution that can be implemented in various healthcare settings, even those with limited resources.

[0020] Another important goal of the invention is to improve the interpretability of machine learning results. By designing the system to provide clear and understandable prognostic reports, the invention enables healthcare professionals to understand and trust the underlying decision-making process. This transparency improves the usability of AI-driven systems in clinical practice and promotes collaboration between human experts and machine learning models. SHORT DESCRIPTION OF THE FIGURE

[0021] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of a system for improved breast cancer prognosis using multimodal machine learning.

[0022] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawing may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. Detailed description of the invention:

[0023] To facilitate an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation on the scope of the invention is intended thereby, since such changes and further modifications of the illustrated system, and such further applications of the principles of the invention as illustrated therein, are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0024] It will be understood by one skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0025] References in this specification to "one aspect," "another aspect," or similar expressions mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the terms "in one embodiment," "in another embodiment," and similar expressions throughout this specification may or may not all refer to the same embodiment.

[0026] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0027] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0028] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0029] Fig.shows a block diagram of a system for improved breast cancer prognosis using multimodal machine learning, the system comprising: a central processing unit (CPU) 102 configured to coordinate and execute data processing tasks; a multimodal data acquisition interface 104 in communication with multiple diagnostic devices and hospital databases, the multimodal data acquisition interface configured to receive various data sets, including mammography images, histopathology images, genetic profiles, and medical records;a feature extraction unit 106 operatively connected to the central processing unit, the feature extraction unit configured to apply deep learning models to extract relevant features from the received data sets, including cell structures from histopathological images, masses or calcifications from mammographic images, and genetic mutations from genetic profiles;a machine learning engine 108 integrated with the central processing unit and the feature extraction unit, wherein the machine learning engine 108 is configured to analyze the extracted features using multimodal learning techniques, wherein the machine learning engine 108 is further configured to combine data from multiple modalities to generate a prognosis for breast cancer, wherein the prognosis includes an assessment of disease progression, risk of recurrence, and likely treatment response; a prognosis reporting unit 110 in communication with the machine learning engine 108, wherein the prognosis reporting unit is configured to generate a comprehensive prognosis report in visual and textual formats, the report presenting the results of the multimodal analysis in a format interpretable by medical professionals;and a power supply unit 112 configured to continuously supply power to the system, the power supply unit further comprising an uninterruptible power supply (UPS) to ensure uninterrupted operation of the system in the event of power outages or fluctuations;

[0030] In one embodiment, the multimodal data acquisition interface 104 includes multiple input ports, including imaging ports configured to accept data from histopathology and mammography devices, network interfaces for retrieving clinical and genetic data from hospital databases, and a real-time data streaming interface configured to process live diagnostic data inputs for immediate analysis.

[0031] In one embodiment, the feature extraction unit 106 is equipped with a graphical processing unit (GPU) and a tensor processing unit (TPU) designed to accelerate the deep learning models used for feature extraction, enabling the system to process large data sets in real time without delays, and wherein the GPU and TPU are operatively connected to the central processing unit to ensure seamless data flow between the units.

[0032] In one embodiment, the machine learning engine 108 includes an ensemble of deep learning techniques, convolutional neural networks (CNNs) for image data analysis, support vector machines (SVMs) for classifying clinical data, and natural language processing (NLP) models for analyzing clinical records, where the engine is further configured to continuously adapt and optimize its predictive models based on incoming data to improve the accuracy of breast cancer prognosis over time.

[0033] In one embodiment, the prognostic reporting unit 110 further includes a high-resolution display and a touchscreen interface, the interface being configured to allow healthcare professionals to interact with the system, view the detailed prognostic reports, and adjust the system's machine learning parameters, and the reports generated by the system can also be securely transmitted to other hospital systems via network interfaces integrated into the prognostic reporting unit.

[0034] In one embodiment, the power supply unit 112 is further configured to monitor the energy consumption of the system, wherein the uninterruptible power supply (UPS) can ensure continuity of operation of at least 8 hours in the event of a power failure to ensure that ongoing predictive analyses are not interrupted.

[0035] In one embodiment, the central processing unit 102 is configured to facilitate parallel processing of data, enabling the system to analyze multiple data sets from different modalities simultaneously, and the CPU is further configured to manage the integration of new data sources or diagnostic modalities as the system evolves, thereby maintaining adaptability to future medical technologies.

[0036] The system for enhanced breast cancer prognosis uses multimodal machine learning and leverages various data inputs, including medical images, clinical records, genetic markers, and additional data points reflecting the patient's physiological and pathological status. The multimodal nature of the system enables a comprehensive understanding of the disease state and contributes to a more precise and personalized prognosis.

[0037] The data acquisition interface collects data from various sources, including histopathological images acquired with digital pathology systems, mammograms, genetic profiles from genome sequencing, and clinical records from hospital information systems. The system ensures seamless integration of these diverse data formats and provides a unified input for further processing.

[0038] A feature extraction module is used to analyze raw data from various modalities. In histopathological images, the module uses convolutional neural networks (CNNs) to detect critical features such as cellular structures, tissue patterns, and tumor boundaries. Features such as calcifications, asymmetries, and masses are extracted from mammographic data. From genetic data, the system identifies genetic mutations and expressions associated with breast cancer prognosis. Clinical data is analyzed using natural language processing (NLP) techniques to identify relevant medical histories, treatment records, and patient demographics. This extracted data is then normalized and structured for use in the machine learning engine.

[0039] The machine learning engine includes deep learning models for image processing, support vector machines (SVMs) for clinical data classification, and ensemble learning techniques that synthesize predictions from multiple modalities. The machine learning engine is designed to be self-adaptive and updates its techniques based on new input data to improve their accuracy over time.

[0040] A central processing unit (CPU) orchestrates the entire system operation. It handles data input and output, controls the flow of information between the feature extraction module, the machine learning engine, and the prediction reporting unit, and ensures the smooth operation of the system under various load conditions. The CPU uses parallel processing techniques to efficiently manage large data sets from various modalities, thus reducing the time required to produce accurate predictions.

[0041] The prognostic reporting unit provides the final prognostic results in a user-friendly format suitable for healthcare professionals. It can generate detailed reports containing risk assessments, likely treatment outcomes, and long-term survival prognoses. The system allows for customization of the report format, ensuring it meets the specific needs of oncologists and other healthcare providers.

[0042] Furthermore, the system can be deployed in a distributed architecture to facilitate large-scale implementations, such as integration with hospital information systems (HIS) or cloud-based platforms that enable remote access for physicians. The system's adaptability ensures that it remains relevant even as new diagnostic technologies and data types become available, making it future-proof and scalable.

[0043] The device features a multimodal data acquisition interface that enables seamless data input from various diagnostic devices and hospital systems. This interface is equipped with multiple ports designed for processing mammography images, histopathology data, and other medical diagnoses. It also features network interfaces that enable secure connections to hospital databases, facilitating the retrieval of clinical and genetic information.

[0044] It has an integrated storage unit for storing large data sets, including high-resolution images, genetic profiles, and patient records. This storage system is optimized with fast-access memory to ensure data retrieval and processing without delays, even with large amounts of information.

[0045] To further enhance system performance, the device features a Machine Learning Accelerator, specifically designed to increase the speed and efficiency of machine learning computations. This accelerator leverages graphics processing units (GPUs) and tensor processors (TPUs) to accelerate the execution of deep learning models, especially in real-time data analysis and decision-making scenarios.

[0046] Healthcare professionals interact with the device via a user interface and display module featuring a high-resolution touchscreen. This intuitive interface allows users to enter patient data, adjust machine learning parameters, and review detailed prognostic reports generated by the system. The interface is designed to be user-friendly, ensuring healthcare professionals can quickly access the system's advanced features.

[0047] The prognostic reporting unit processes the results of the machine learning processes and generates visual and text reports for healthcare providers. These reports, which provide detailed insights into breast cancer prognosis, are presented using advanced data visualization techniques to make complex machine learning results easily interpretable. The reports can be printed or securely transmitted electronically to other healthcare systems.

[0048] To ensure continuous operation, especially in critical healthcare environments, the device is equipped with an uninterruptible power supply (UPS). This component protects against power outages or fluctuations, ensuring the device remains operational at all times and uninterrupted operation in hospitals or research environments.

[0049] The system for enhanced breast cancer prognosis using multimodal machine learning aims to improve the accuracy and reliability of prognosis predictions by integrating multiple data types such as mammography images, histopathology images, genetic profiles, and clinical records. The central processing unit (CPU) plays a critical role in orchestrating all data processing tasks within the system. It is responsible for managing the input, processing, and output of data from the multimodal data acquisition interface, the feature extraction unit, the machine learning engine, and the prognosis reporting unit.

[0050] The multimodal data acquisition interface connects to various diagnostic devices and hospital databases, allowing the system to receive diverse data sets required for comprehensive analysis. The interface is equipped with multiple input ports tailored to different data sources. Imaging ports allow the system to receive high-resolution histopathological and mammographic images, while network interfaces retrieve genetic and clinical data from hospital databases. Furthermore, the interface features real-time data streaming capability, allowing live input from diagnostic devices, enabling immediate analysis for cases requiring urgent prognostic assessment.

[0051] The feature extraction unit is central to processing the raw data acquired by the acquisition interface. It is equipped with powerful graphics processing units (GPUs) and tensor processors (TPUs) to accelerate the application of deep learning models. The feature extraction process begins with the analysis of histopathological images using convolutional neural networks (CNNs) specifically trained to identify key features such as cellular structures, tissue patterns, and tumor boundaries. Similarly, CNNs are used on mammography images to detect calcifications, asymmetries, and masses indicative of breast cancer. Genetic profiles are analyzed using machine learning models that can identify mutations, gene expression, and other molecular markers related to cancer prognosis.Clinical records, including patient history and treatment data, are processed using natural language processing (NLP) techniques that extract relevant information such as previous diagnoses, treatment responses, and comorbidities.

[0052] Once the raw data has been processed by the feature extraction unit, the output is passed to the machine learning engine, where the actual predictive analysis takes place. The machine learning engine is configured to analyze the extracted features from different data modalities. The engine uses an ensemble of models, including convolutional neural networks (CNNs) for image-based data, support vector machines (SVMs) for classifying clinical data, and advanced natural language processing (NLP) techniques for interpreting clinical text recordings. The ensemble approach enables the machine learning engine to effectively handle the multimodal nature of the input data and identify correlations between different data types that are often missed by single-modality analytics systems.

[0053] The machine learning engine is designed to dynamically adapt to new input data, continuously improving its predictive capabilities. The engine uses a combination of supervised and unsupervised learning models trained on historical breast cancer datasets. Supervised learning models are trained to identify specific patterns in the input data associated with known outcomes, such as the likelihood of recurrence or response to treatment. Unsupervised learning models, on the other hand, explore hidden patterns and relationships within the data without predefined outcomes. This combination of learning techniques enables the system to provide more comprehensive prognostic predictions that account for the complexity and variability of breast cancer cases.

[0054] In addition, the machine learning engine uses a weighted fusion technique to integrate the predictions from different modalities into a unified forecast.

[0055] Each modality—whether imaging, genetic, or clinical—contributes a certain weight to the overall prediction based on the confidence level of the data. For example, if histopathological data indicate highly malignant features, they may be assigned a higher weight in the final prognosis than clinical records that do not reveal a significant history of breast cancer. This weighted fusion ensures that the contribution of each data type is carefully calibrated based on its relevance to the specific case, allowing the system to be tailored to individual patient characteristics.

[0056] The prognostic reporting unit is responsible for presenting the results of the machine learning analysis in a user-friendly format. It generates both visual and text reports that are interpretable by healthcare professionals. The visual reports use advanced data visualization techniques to depict the relationships between the extracted features and the prognostic results, such as a risk graph that shows the probability of relapse based on the integration of multiple data types. The text reports summarize the results and provide a comprehensive overview of the patient's prognosis, including predictions regarding disease progression, treatment response, and estimates of long-term survival. The reports can be securely transmitted to hospital systems or directly accessed by healthcare professionals via the integrated touchscreen display.

[0057] The system is equipped with a power supply that includes an uninterruptible power supply (UPS), ensuring continuous operation even during power outages. This feature is particularly important in healthcare facilities where uninterrupted access to diagnostic and prognostic tools is critical.

[0058] Furthermore, the central processing unit (CPU) is designed to facilitate parallel processing of multimodal data, allowing the system to perform simultaneous analyses from multiple data sources. This parallel processing capability significantly reduces the time required for prognosis generation, making the system suitable for demanding environments such as hospitals. The CPU also ensures the system's adaptability to future advances in diagnostic technology by enabling the integration of new data sources and modalities as they become available, ensuring the system remains relevant as medical research evolves.

[0059] The system offers a comprehensive, multimodal approach to breast cancer prognosis, leveraging advanced feature extraction and machine learning techniques to integrate diverse data types and generate highly accurate and personalized prognostics. The system is designed to enhance existing solutions by providing a more comprehensive overview of the patient's disease status, ensuring timely and accurate treatment decisions, and continuously adapting to new medical data and diagnostic technologies.

[0060] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0061] Advantages, other benefits, and solutions to problems have been described above with reference to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage, benefit, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 A system for improved breast cancer prognosis using multimodal machine learning. 102 Central processing unit (CPU) 104 Multimodal data acquisition interface 106 Feature extraction unit 108 Machine Learning 110 Forecast Reporting Office 112 Power supply

Claims

[1] A system for improved breast cancer prognosis using multimodal machine learning, the system includes: a central processing unit (CPU) configured to coordinate and execute data processing tasks; a multimodal data acquisition interface for communication with multiple diagnostic devices and hospital databases, the multimodal data acquisition interface being configured to receive different data sets, including mammography images, histopathological images, genetic profiles and medical records, a feature extraction unit operatively connected to the central processing unit, the feature extraction unit configured to apply deep learning models to extract relevant features from the received data sets, including cell structures from histopathological images, masses or calcifications from mammographic images, and genetic mutations from genetic profiles; a machine learning engine integrated with the central processing unit and the feature extraction unit, the machine learning engine configured to analyze the extracted features using multimodal learning techniques, the machine learning engine further configured to combine data from multiple modalities to generate a prognosis for breast cancer, the prognosis including an assessment of disease progression, recurrence risk, and likely treatment response; a prognostic reporting unit in communication with the machine learning engine, the prognostic reporting unit configured to generate a comprehensive prognostic report in both visual and text formats, the report presenting multimodal analysis results in a format interpretable by healthcare professionals; and a power supply configured to continuously supply power to the system, wherein the power supply also includes an uninterruptible power supply (UPS) to ensure uninterrupted operation of the system during power failures or fluctuations. [2] The system of claim 1, wherein the multimodal data acquisition interface comprises a plurality of input ports, including imaging ports configured to accept data from histopathology and mammography devices, network interfaces for retrieving clinical and genetic data from hospital databases, and a real-time data streaming interface configured to process live diagnostic data inputs for immediate analysis. [3] The system of claim 1, wherein the feature extraction unit is equipped with a graphics processing unit (GPU) and a tensor processing unit (TPU) configured to accelerate the deep learning models used for feature extraction so that the system can process large data sets in real time without delays, and wherein the GPU and the TPU are operatively connected to the central processing unit to ensure seamless data flow between the units. [4] The system of claim 1, wherein the machine learning engine comprises an ensemble of deep learning techniques, convolutional neural networks (CNNs) for image data analysis, support vector machines (SVMs) for classifying clinical data, and natural language processing (NLP) models for analyzing clinical records, the engine further configured to continuously adapt and optimize its predictive models based on incoming data to improve the accuracy of breast cancer prognosis over time. [5] The system of claim 1, wherein the prognostic reporting unit further comprises a high-resolution display and a touchscreen interface, the interface being configured to allow healthcare professionals to interact with the system, view the detailed prognostic reports, and adjust the machine learning parameters of the system, wherein the reports generated by the system can also be securely transmitted to other hospital systems via network interfaces integrated into the prognostic reporting unit. [6] The system of claim 1, wherein the power supply unit is further configured to monitor the energy consumption of the system, wherein the uninterruptible power supply (UPS) can ensure continuity of operation of at least 8 hours in the event of a power failure, thereby ensuring that ongoing predictive analyses are not interrupted. [7] The system of claim 1, wherein the central processing unit is configured to facilitate parallel processing of data, enabling the system to analyze multiple data sets from different modalities simultaneously, and wherein the CPU is further configured to manage the integration of new data sources or diagnostic modalities as the system evolves, thereby maintaining adaptability to future medical technologies.

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