A SYSTEM AND METHOD FOR AI-ENHANCED DIAGNOSIS AND PROGNOSIS OF OVARIAN CANCER USING HISTOPATHOLOGY

Abstract

ABSTRACT The present invention relates to a system and method for AI-enhanced diagnosis and prognosis of ovarian cancer using histopathology. The system includes a digital pathology imaging system for capturing high-resolution images of ovarian tissue, a preprocessing unit for preparing the images, and an AI model for analyzing the preprocessed images to detect, segment, and classify tumor regions based on histological features. The system generates diagnostic predictions, including tumor subtype, grade, and prognostic insights, which are displayed on a user interface for review by pathologists. Additionally, the invention incorporates a real-time analysis module for intraoperative diagnostics, allowing for immediate decision-making during surgeries. As such, the present invention offers significant improvements in diagnostic accuracy, workflow efficiency, and personalized treatment planning, ultimately leading to better patient outcomes in ovarian cancer care. FIG. 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to a system and method for diagnosing and prognosing ovarian cancer using artificial intelligence and digital histopathology images, and more particularly to enhancing the accuracy and efficiency of histopathological analysis through advanced image processing techniques. Further, the present invention discloses a comprehensive approach that integrates multi-modal data, adaptive learning, and predictive modeling to improve patient-specific treatment planning and outcomes.
BACKGROUND
[0002] In the rapidly evolving landscape of cancer diagnosis and treatment, ovarian cancer remains one of the most challenging malignancies due to its late-stage detection and high mortality rate. Current diagnostic practices primarily rely on histopathological examination of tissue samples, which requires specialized knowledge and significant time investment. Despite advancements in imaging technology and diagnostic tools, the subjective nature of conventional histopathology can lead to variability in interpretation among pathologists, often resulting in misdiagnosis or delayed treatment.
[0003] Conventional methods available for the diagnosis of ovarian cancer include histological evaluation of tissue stained with haematoxylin and eosin (H&E) and additional ancillary tests such as immunohistochemistry (IHC). However, these methods are time-consuming and can be prone to inter-observer variability. General pathologists may seek the expertise of subspecialty gynecological pathologists for difficult cases, which can prolong the diagnostic process and increase healthcare costs. Moreover, there is a shortage of qualified pathologists, further complicating timely diagnosis and effective treatment.
[0004] Furthermore, the existing system merely focuses on image-based analysis without leveraging additional patient-specific data, such as genetic information or clinical history, to enhance diagnostic accuracy. Current AI implementations in histopathology often lack adaptive learning capabilities, limiting their effectiveness in dynamic clinical environments where continuous data input is essential for maintaining diagnostic precision.
[0005] As a result, there is a need for a system and method that integrates artificial intelligence with comprehensive data sources to improve the accuracy, efficiency, and overall effectiveness of ovarian cancer diagnosis and prognosis, thereby facilitating better patient outcomes and streamlined workflows in pathology labs.
[0006] Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through the comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.
SUMMARY
[0007] In an embodiment, a method for AI-assisted diagnosis and prognosis of ovarian cancer through digital histopathology images is disclosed. In one example, the method includes receiving digitized histopathology images of ovarian tissue samples and preprocessing these images through normalization, scaling, and noise reduction. Further, the method applies a deep learning model, such as a convolutional neural network (CNN), to detect and classify tissue abnormalities, including distinguishing between benign and malignant cells and identifying cancer subtypes. The method also integrates patient-specific data, such as clinical history and genetic information, to refine predictive analysis for treatment responses and survival rates. Finally, the method provides an interactive interface for pathologists to review AI-generated results, validate findings, and make real-time adjustments, thereby enabling a more accurate and efficient diagnostic workflow.
[0008] In an embodiment, a system for AI-enhanced diagnosis and prognosis of ovarian cancer using digital histopathology images is disclosed. In one example, the system comprises an input module for receiving and digitizing histopathology images, a preprocessing unit for scaling, normalization, and noise reduction, and an AI processing module equipped with deep learning models, such as convolutional neural networks (CNNs), for feature extraction and classification of cancerous tissues. Further, the system includes a data integration module that combines patient-specific data, including genetic information and clinical history, to refine diagnostic accuracy and predict treatment outcomes. An interactive user interface allows pathologists to visualize and verify AI-generated results, while a cloud-based storage unit securely maintains patient data and analysis history for ongoing reference and research.
BRIEF DESCRIPTION OF DRAWINGS
[0009] The accompanying drawings illustrate the various embodiments of systems, methods, and other aspects of the disclosure. Any person with ordinary skills in the art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Further, the elements may not be drawn to scale.
[0010] Various embodiments will hereinafter be described in accordance with the appended drawings, which are provided to illustrate and not to limit the scope in any manner, wherein similar designations denote similar elements, and in which:
[0011] FIG. 1 is a block diagram illustrating the system environment 100 in which various embodiments of the present invention may be implemented.
[0012] FIG. 2 is a block diagram illustrating the architecture of Processing Unit 104 configured for the AI-assisted ovarian cancer diagnosis system, in accordance with an embodiment of the present invention.
[0013] FIG. 3 is a flowchart that illustrates a method for enhancing the diagnosis and prognosis of ovarian cancer using AI-powered histopathology analysis, in accordance with an embodiment of the present invnetion.
DETAILED DESCRIPTION
[0014] The present disclosure may be best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.
[0015] References to "one embodiment," "at least one embodiment," "an embodiment," "one example," "an example," "for example," and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Further, repeated use of the phrase "in an embodiment" does not necessarily refer to the same embodiment.
[0016] The present invention addresses the limitations of conventional histopathological analysis for ovarian cancer diagnosis, which often relies on time-intensive, subjective interpretation by pathologists, leading to variability and potential delays in treatment. The present system leverages advanced artificial intelligence to streamline the diagnostic process by automatically analyzing digital histopathology images, identifying cancerous tissues, and classifying ovarian cancer subtypes with high accuracy. Further, the present system integrates deep learning models, such as convolutional neural networks, to enhance detection capabilities and combines patient-specific clinical and genetic data to deliver more precise prognostic insights. Additionally, it provides a user-friendly interface for pathologists to review AI-driven predictions and adjust as needed, ensuring a seamless, accurate, and efficient diagnostic workflow that significantly reduces pathologist workload and improves patient outcomes.
[0017] The primary objective of the present invention is to improve the accuracy, efficiency, and consistency of ovarian cancer diagnosis and prognosis by integrating artificial intelligence with digital histopathology. To achieve this, the present disclosure aims to reduce diagnostic variability, minimize delays in treatment initiation, and provide pathologists with reliable, data-driven insights that support rapid and accurate decision-making. The system's objective is to automate the analysis of histopathology images, detect and classify ovarian cancer subtypes, and incorporate patient-specific data for personalized prognostic assessments. Additionally, the present disclosure seeks to alleviate pathologist workload by offering a real-time, user-friendly interface for verifying AI-generated results, supporting continuous learning through regular system updates, and facilitating more precise treatment planning to enhance overall patient care and outcomes.
[0018] The present invention introduces a novel system and method for AI-assisted diagnosis and prognosis of ovarian cancer, leveraging advanced deep learning models integrated with comprehensive patient data to achieve enhanced diagnostic accuracy and personalized treatment planning. The present system combines digital histopathology image analysis with patient-specific clinical and genetic information, allowing for more precise identification and classification of ovarian cancer subtypes and prognosis. Its adaptive learning capability, which continuously updates the AI model based on new cases and emerging research, maintains its accuracy and clinical relevance over time. Additionally, this system incorporates real-time diagnostic support, enabling pathologists to make intraoperative decisions, such as assessing tumor margins during surgery. By embedding AI into the pathology workflow, the present invention significantly reduces inter-observer variability, accelerates the diagnostic process, and enables scalable, high-throughput analysis, addressing key limitations of traditional histopathology.
[0019] FIG. 1 is a block diagram illustrating the system environment 100 in which various embodiments of the present invention for AI-assisted ovarian cancer diagnosis and prognosis may be implemented. The system environment 100 generally comprises an image acquisition unit 102, a preprocessing unit 104, an AI analysis module 106, a data integration module 108, a pathologist interface 110, and a data storage unit 112. These components are interconnected through a secure communication network 114, ensuring seamless data flow and operational integration across the system.
[0020] As according to the present invention, the image acquisition unit 102 captures and digitizes histopathology images from tissue samples. The said unit includes a high-resolution scanner or camera to generate detailed digital images of biopsy slides stained with haematoxylin and eosin (H&E). These images form the basis for AI analysis and are transferred to the preprocessing unit 104 for further processing.
[0021] As according to the present invention, the preprocessing unit 104 is responsible for preparing the histopathology images for AI processing. This includes functions such as image normalization, noise reduction, and scaling, ensuring consistency and accuracy. The preprocessing unit 104 applies advanced filtering techniques to enhance image clarity, which is critical for precise AI interpretation.
[0022] As according to the present invention, the AI analysis module 106 incorporates deep learning models, including convolutional neural networks (CNNs), for automated feature extraction, classification, and segmentation of tissue images. The said module identifies and categorizes cancerous tissues, determining ovarian cancer subtypes and grading severity levels. The AI analysis module 106 continuously learns from new datasets, enhancing its diagnostic accuracy and adaptability with each iteration.
[0023] As according to the present invention, the data integration module 108 combines diagnostic findings from the AI analysis module 106 with patient-specific data, such as genetic markers, clinical history, and other relevant medical information. This fusion enables a more holistic diagnosis, allowing for personalized treatment planning and refined prognostic predictions. By integrating multifaceted data, the module enhances the clinical applicability of the system.
[0024] As according to the present invention, the Pathologist Interface 110 is a user-friendly platform where pathologists can review AI-generated diagnoses, verify findings, and make any necessary adjustments. This interface includes interactive visualization tools, enabling pathologists to observe detailed image annotations and segmentation results. It also provides options for real-time adjustments, allowing pathologists to tailor the output to specific diagnostic needs.
[0025] As according to the present invention, the data storage unit 112 securely archives all patient data, histopathology images, AI analysis results, and diagnostic findings. The said unit supports both cloud-based and on-premises storage, ensuring compliance with data protection regulations. The data storage unit 112 also allows for future case referencing and contributes to continuous system learning by providing a large, labeled dataset for model retraining.
[0026] As according to the present invention, the communication network 114 facilitates seamless data exchange across the system components, using secure protocols to ensure data integrity and privacy. It supports various communication standards, including Wi-Fi, Ethernet, and cloud connectivity, enabling real-time data transmission between modules. Network 114 allows for integration with external systems, such as laboratory information systems (LIS), enabling automated data transfer and system updates.
[0027] FIG. 2 is a block diagram illustrating the architecture of Processing Unit 104 configured for the AI-assisted ovarian cancer diagnosis system, in accordance with an embodiment of the present invention. The processing unit 104, as depicted in conjunction with elements from FIG. 1, includes a processor 202, memory 204, transceiver 206, and input/output interface 208. The processor 202 is interconnected with image acquisition components, an AI module for feature extraction 210, a clinical data integration unit 212, a diagnostic prediction unit 214, and a pathologist support interface 216. These elements work in unison to ensure efficient data handling, processing, and diagnostic feedback.
[0028] As according to the present invention, the processor 202 is configured with advanced computing capabilities and operates by executing specialized algorithms stored in memory 204. The present processing capability allows for complex tasks such as real-time image analysis, machine learning-based feature extraction, and integration with external databases. The processor 202 is responsible for managing the computational tasks needed to analyze the histopathology images and clinical data, offering robust diagnostic support through continuous AI learning. The processor may employ advanced architectures, including multi-core setups or GPU-accelerated computing, to expedite image processing tasks and improve prediction accuracy.
[0029] As according to the present invention, the memory 204 is designed to store a wide array of diagnostic data, including training datasets, image processing algorithms, and patient-specific information. It accommodates both short-term operational data and long-term storage for model training and historical diagnostic results, ensuring that the system can reference past cases and continuously refine its analytical capabilities. Memory 204 may include RAM, SSDs, or cloud-based storage to enable scalable data access and secure archiving.
[0030] As according to the present invention, the transceiver 206 manages communication between the processing unit and external systems, such as medical databases, laboratory information systems, and pathologists' remote interfaces. The said component ensures secure data transmission, enabling the system to send analysis results, diagnostic reports, and alerts to pathologists or healthcare providers. It supports wireless and wired connections, leveraging encryption protocols to protect patient data while allowing seamless data integration with hospital infrastructure or telemedicine networks.
[0031] As according to the present invention, the input/output interface 208 includes mechanisms for interfacing with pathologists and other users, enabling real-time access to diagnostic insights, interaction with AI-derived suggestions, and feedback on detected anomalies. This interface may incorporate a display screen for visualizing histopathology images with annotations, controls for navigating AI-suggested features, and options to interactively adjust diagnostic settings.
[0032] As according to the present invention, the AI module for feature extraction 210 utilizes deep learning techniques, such as convolutional neural networks (CNNs), to identify and classify cancerous cells, cell clusters, and tissue structures from histopathology images. The said module is integrated with the processor 202 and receives real-time data from the image acquisition unit to detect abnormal patterns with high precision, helping to differentiate between benign and malignant tissues.
[0033] As according to the present invention, the clinical data integration unit 212 aggregates patient-specific data such as clinical history, biomarkers, and genetic information with the imaging data to provide a comprehensive analysis. The said unit enables the system to consider multiple diagnostic factors, enhancing prediction accuracy and offering insights aligned with personalized treatment plans.
[0034] As according to the present invention, the diagnostic prediction unit 214 processes combined imaging and clinical data, generating predictive models that estimate cancer progression and treatment outcomes. It employs advanced machine learning models to analyze longitudinal data and provides probability-based insights on diagnosis, assisting pathologists in evaluating potential treatment options.
[0035] As according to the present invention, the pathologist support interface 216 allows pathologists to review AI-generated analyses and make adjustments if needed. The said component includes interactive tools for visualizing segmented images, annotating findings, and validating AI predictions, offering enhanced support in complex diagnostic cases. The interface facilitates user-friendly access to both historical and current case data, enabling pathologists to efficiently navigate patient-specific diagnostic workflows.
[0036] In an exemplary operation, a system to assist in the early diagnosis of ovarian cancer operates through real-time data processing and advanced AI-driven analysis. The system comprises a histopathology image acquisition unit that captures high-resolution images of tissue samples, a processing unit equipped with deep learning modules for feature extraction and classification, and a clinical data integration unit for incorporating patient-specific medical history and biomarker data. The system comprises communication modules that enable secure data transmission between the diagnostic system and remote healthcare providers. In an embodiment, the processing unit utilizes a convolutional neural network (CNN) model to analyze cellular structures and detect malignant patterns within histopathology images. In an embodiment, the clinical data integration unit augments image-based findings by incorporating relevant clinical indicators, offering a comprehensive diagnostic profile for each patient. In an embodiment, the system provides an interactive interface for pathologists, enabling them to review AI-generated analyses, apply annotations, and adjust diagnostic parameters in real-time. In another embodiment, a feedback mechanism within the system refines AI modules by learning from pathologist inputs, continuously improving diagnostic accuracy and predictive capabilities. Such an integrated framework enables the system to deliver highly accurate, data-driven insights to pathologists and oncologists, significantly enhancing the diagnostic process for ovarian cancer.
[0037] In an embodiment, the processor is configured to analyze high-resolution histopathology images by applying advanced convolutional neural networks (CNNs) to detect early markers of ovarian cancer. In an embodiment, the processor is configured to extract cellular and tissue features such as cell morphology, nuclear irregularities, and tissue organization, enabling precise differentiation between benign and malignant samples. In an embodiment, the processor is configured to integrate patient-specific clinical data with image-based findings, utilizing a multimodal approach for enhanced diagnostic accuracy. Additionally, the processor is configured to implement a feedback loop that incorporates expert annotations from pathologists, continuously refining its predictive models for improved sensitivity and specificity over time. In another embodiment, the processor is configured to generate visual heat maps overlaid on histopathology images, highlighting regions of interest and providing pathologists with interpretive assistance for complex cases. Furthermore, the processor is configured to communicate diagnostic results and insights to connected healthcare systems via secure data channels, facilitating collaboration and enabling remote diagnostic consultations. Through these configurations, the processor ensures comprehensive and real-time analysis, supporting a robust and data-driven approach to early ovarian cancer diagnosis.
[0038] In another embodiment, the present invention provides a system configured to assess and classify tumor tissue samples using machine learning modules integrated within a digital pathology framework. The system is designed to capture and preprocess histopathology images, automatically calibrating for variations in staining and magnification to ensure consistent analysis. The processor then applies a combination of supervised and unsupervised learning techniques to recognize distinct cellular patterns and morphologies that correspond to specific tumor grades. Additionally, the system utilizes a deep learning model to analyze both cellular and extracellular regions, distinguishing features like necrosis, mitotic activity, and stromal response, which are critical for accurate tumor grading and prognosis. The system further generates a detailed pathology report that includes visual annotations and quantitative metrics, aiding pathologists in decision-making. Moreover, this embodiment supports integration with hospital databases, allowing for cross-referencing of similar cases, and incorporates encryption protocols to maintain data privacy. Through this comprehensive approach, the system enables precise, automated, and secure tumor assessment, enhancing diagnostic efficiency and accuracy for medical professionals.
[0039] Let us consider a practical scenario to illustrate the workings of the present invention. Consider a pathology laboratory where a pathologist receives a digital slide image of a patient's ovarian tissue for analysis. The image, stained with haematoxylin and eosin (H&E), is fed into an AI-powered system integrated with the laboratory's digital pathology platform. The AI model, trained on a vast dataset of annotated ovarian cancer images, automatically processes the slide to detect potential malignancies and categorize the tissue into different ovarian cancer subtypes, such as high-grade serous carcinoma or endometrioid carcinoma. The system segments the tumor regions and provides a preliminary diagnosis, including tumor grading and prognosis predictions. The pathologist reviews the AI-generated results, validates the findings, and provides the final diagnosis. The AI system not only speeds up the diagnostic process by automating time-consuming tasks but also ensures greater accuracy, reducing inter-observer variability and enhancing overall diagnostic confidence.
[0040] FIG. 3 is a flowchart that illustrates a method for enhancing the diagnosis and prognosis of ovarian cancer using AI-powered histopathology analysis, in accordance with an embodiment of the present invention. The method begins in a Start step 302 and proceeds to step 304. At step 304, the digital pathology image of the ovarian tissue (e.g., H&E stained slide) is received by the system. Next, at step 306, the image undergoes preprocessing, which includes normalization, denoising, and scaling to prepare the image for AI model analysis. Step 308 follows, where the AI model (e.g., convolutional neural network) is applied to the preprocessed image to perform tumor detection, segmentation, and classification based on histological features. In step 310, the system generates a preliminary diagnosis, including tumor subtype, grade, and prognostic predictions, which are then displayed on the user interface for pathologist review. At step 312, the pathologist reviews the AI-generated results and validates the findings, adjusting the diagnosis if necessary. Step 314 involves the final diagnosis being recorded and stored in the data storage system, along with AI analysis history, for future reference. The method concludes at step 316, where the validated diagnosis is used to support treatment planning, and the AI model is periodically retrained with new data to improve its diagnostic capabilities.
[0041] The present invention offers several technical advantages over conventional methods in ovarian cancer diagnosis and prognosis. Its integration of multiple sensor technologies, including digital imaging and AI-powered analysis, significantly enhances diagnostic accuracy and efficiency. Such an enhanced capability allows for faster and more reliable tumor detection, classification, and segmentation, minimizing human error and inter-observer variability. Additionally, the use of deep learning models, such as convolutional neural networks, enables the system to extract complex features from pathology images, providing detailed insights into tumor subtypes and prognostic factors. Furthermore, the seamless integration with existing digital pathology platforms ensures smooth data flow and allows for real-time decision-making during surgeries. The system's ability to automatically preprocess, analyze, and validate results reduces pathologist workload, while continuous retraining with new data ensures the AI model stays current with the latest research, further improving diagnostic accuracy over time. Together, these advancements contribute to more personalized treatment planning, accelerated research, and ultimately better patient outcomes.
[0042] The present invention provides a concrete and tangible solution to a significant technical problem in the field of ovarian cancer diagnosis and prognosis through AI-enhanced histopathology. The present disclosure offers specific technical features and functionalities, such as the integration of digital pathology imaging with AI-driven deep learning models to automatically detect, segment, and classify ovarian tissue based on histological features. The present system improves diagnostic accuracy by reducing human error and inter-observer variability. Additionally, the method incorporates preprocessing steps like normalization, denoising, and scaling to ensure high-quality image input, while real-time analysis during surgeries enables rapid decision-making. The system also supports pathologist collaboration through an interactive user interface for reviewing AI-generated results and provides continuous model retraining with new data to ensure ongoing accuracy. These technical advancements ensure more accurate, efficient, and cost-effective ovarian cancer diagnostics, ultimately leading to improved patient outcomes and faster research and treatment development.
[0043] A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above-disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.
[0044] Those skilled in the art will appreciate that any of the aforementioned steps and/or system modules may be suitably replaced, reordered, or removed, and additional steps and/or system modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable processes and system modules, and are not limited to any particular computer hardware, software, middleware, firmware, microcode, and the like. The claims can encompass embodiments for hardware and software or a combination thereof.
[0045] While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.
, Claims:CLAIMS
We Claim:
1. A device for AI-enhanced diagnosis and prognosis of ovarian cancer using histopathology, the device comprising:
a digital pathology imaging system configured to capture high-resolution images of ovarian tissue stained with haematoxylin and eosin (H&E);
a preprocessing unit configured to process the captured image by performing normalization, denoising, and scaling to prepare the image for AI analysis;
an artificial intelligence (AI) model configured to analyze the preprocessed image, extract features, perform tumor detection, segmentation, and classification based on histological features of the ovarian tissue, and generate diagnostic and prognostic results;
a user interface configured to display diagnostic predictions and prognostic insights, enabling review and validation by a pathologist;
a data storage system configured to store patient records and AI analysis history for future reference; and
a real-time analysis module for intraoperative diagnostics, enabling real-time decision-making during ovarian cancer surgeries.

2. The device of claim 1, wherein the AI model comprises a deep learning model selected from the group consisting of convolutional neural networks (CNN), recurrent neural networks (RNN), and hybrid models thereof.

3. The device of claim 1, further comprising a retraining module configured to periodically update the AI model with new patient data to improve the diagnostic accuracy over time.

4. The device of claim 1, wherein the diagnostic results include tumor classification, tumor grade, and prognostic insights selected from the group consisting of survival rate, response to treatment, and tumor subtype.

5. The device of claim 1, wherein the digital pathology imaging system is integrated with an existing laboratory information system (LIS) or digital pathology platform to ensure seamless data flow without disrupting existing workflow.

6. A method for AI-enhanced diagnosis and prognosis of ovarian cancer using histopathology, the method comprising the steps of:
capturing a high-resolution digital pathology image of ovarian tissue stained with haematoxylin and eosin (H&E);
preprocessing the captured image by performing normalization, denoising, and scaling to prepare the image for AI analysis;
analyzing the preprocessed image using an AI model to extract histological features, detect tumor regions, segment the tumor, and classify the tissue for tumor subtype, grade, and prognosis;
generating diagnostic predictions, including tumor classification, tumor grading, and prognostic insights;
displaying the generated results on a user interface for review and validation by a pathologist;
storing the results and AI analysis history in a data storage system for future reference; and
retraining the AI model periodically with new data to improve the model's diagnostic performance.

7. The method of claim 6, wherein the AI model uses deep learning techniques such as convolutional neural networks (CNN) for feature extraction, classification, and segmentation.

8. The method of claim 6, further uses the AI system for real-time analysis during ovarian cancer surgeries, enabling rapid decision-making regarding the extent of tumor removal.

9. The method of claim 6, wherein the results from the AI model are validated by a pathologist who reviews the AI-generated predictions and makes the final diagnosis.

10. The method of claim 6, wherein preprocessing of the image includes removing noise, normalizing image intensities, and scaling the image to a predetermined resolution suitable for analysis by the AI model.

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