AI-Powered Medical Imaging System for Automated Detection and Measurement of Ovarian Follicles

Healthcare providers face significant time-consuming challenges when manually analyzing ultrasound videos to detect, segment, and measure ovarian follicles. The process involves pausing videos, manually marking follicle dimensions, and measuring parameters such as diameter, surface area, and perimeter, which hampers workflow efficiency and delays diagnosis.

A mid-sized healthcare institution specializing in fertility testing and treatment, seeking to enhance diagnostic accuracy and efficiency in ultrasound analysis.

• Achieve at least 90% precision and 97% recall in follicle detection and measurement.
• Reduce doctors' time spent on manual analysis of ultrasound images and videos.
• Enable processing of videos, images, and folders with minimal manual intervention.
• Streamline reporting by generating analytical reports on system performance and statistical metrics.
• Facilitate integration with existing hospital data systems for seamless workflow.

• Neural network-based follicle detection utilizing advanced object segmentation architectures such as Mask R-CNN.
• Preprocessing modules to enhance image quality and detect anatomical landmarks (e.g., hatch marks) for pixel-to-size calibration.
• Measurement modules that calculate follicle dimensions using pixel size information extracted from ultrasound images.
• Video splitting function to convert video files into image sequences for analysis.
• Support for importing various formats of ultrasound videos and images stored in folders.
• User-friendly interface for clinicians to review detection results, measurements, and generated reports.
• Reporting module that provides analytics including detection accuracy, recall, and other metrics.

• Ultrasound imaging equipment data export interfaces.
• Hospital data systems (e.g., PACS or EMR systems) for integration and data retrieval.
• Reporting tools for export and sharing of analytic results.

• Achieve detection precision above 90% and recall rate above 97%.
• Ensure processing of videos and large datasets within acceptable timeframes.
• Maintain data security and compliance with healthcare data standards (e.g., HIPAA).
• Design for scalability to handle increasing data volumes and concurrent users.
• Ensure system robustness and fault tolerance during operations.

The AI-powered system is expected to significantly enhance diagnostic efficiency by automating follicle detection and measurement with over 90% accuracy, reducing manual analysis time, and enabling quicker clinical decisions. Initial success metrics include a detection precision of 90% and a recall rate of 97%, leading to faster patient assessments and improved workflow productivity. The solution paves the way for broader adoption once validated and approved within healthcare regulatory frameworks.