Deep Learning Model for Gynecologic Tumor Segmentation
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Solution Overview
Problem
There is a need for improved diagnostic and treatment tools in gynecological practices, particularly for uterine fibroids and ovarian tumors, to address health disparities and provide unbiased referrals, as current methods are inefficient and often result in inappropriate surgical approaches for low socio-economic populations.
Innovation Solution
The development of AI-based deep learning models that analyze MRI scans to identify spectral and spatial features of uterine fibroids and ovarian tumors, enabling multi-class segmentation and classification, which helps determine the success rate of minimally invasive procedures and provides mixed reality guidance for gynecological procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models with multi-class segmentation are used to analyze gynecological images, then diagnostic accuracy and surgical planning quality improve, but device complexity and computational requirements increase
Solution Approach 1:
The deep learning model performs multi-class segmentation of gynecological images, dividing the image into distinct anatomical regions and tumor types (e.g., fibroids, ovarian tumors, endometrial lesions). This segmentation enables precise identification and classification of different tissue types, directly improving diagnostic accuracy while managing complexity through specialized processing of each segment separately
Solution Approach 2:
The system transitions from 2D medical images to 3D visualizations by generating three-dimensional representations of tumors and anatomical structures. This dimensional transformation provides surgeons with intuitive spatial understanding of tumor location, size, and relationship to surrounding organs, significantly enhancing surgical planning without requiring complex manual measurements
2Reliability
If automated AI tools are implemented to provide unbiased referrals, then health disparities are reduced and access to minimally invasive procedures improves, but implementation cost and system complexity increase
Solution Approach 1:
The AI system automatically analyzes patient data, generates diagnostic reports, and provides surgical recommendations without requiring manual intervention. The system self-evaluates image quality, automatically segments tumors, and generates standardized referrals, reducing dependency on individual expert availability and ensuring consistent, unbiased decision-making across diverse patient populations
Solution Approach 2:
The system incorporates feedback mechanisms where model predictions are continuously refined based on comparison with ground-truth classifications and expert validations. This feedback loop improves referral accuracy over time while the system learns to recognize patterns across different patient demographics, ensuring equitable treatment recommendations
3Productivity
If 3D visualizations and mixed reality guidance are used during surgery, then surgical efficiency and safety improve, but device complexity and operational requirements increase
Solution Approach 1:
The system performs all image analysis, tumor segmentation, and 3D model generation before the surgical procedure begins. Surgeons receive pre-operative roadmaps showing tumor locations, vascular structures, and optimal incision paths, allowing them to mentally prepare and plan the surgical approach without encountering complexity during the actual procedure
Solution Approach 2:
The 3D visualization system acts as an intermediary between the complex AI analysis engine and the surgeon. It translates complex multi-dimensional data into intuitive visual representations that are easy to interpret during surgery, bridging the gap between sophisticated computational power and human cognitive processing without requiring the surgeon to directly interact with complex algorithms
Data Source
AI summary
Embodiments described herein provide systems and methods for improving diagnosis, screening, and treatment of patients. The disclosed systems and methods generally relate to artificial intelligence (AI) based deep learning models that can help with decision making, for example, in gynecological procedures. In various embodiments, a method of generating a model for performing gynecologic procedures is described. In various embodiments, a method of determining a success rate of a minimally invasive procedure for a patient is described. In various embodiments, a method of enhancing a diagnosis of an ovarian tumor is described. In various embodiments, a method of providing a mixed reality guidance for performing gynecological procedures is described.