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AI-Powered Ovarian Follicle Detection and Measurement System for Healthcare Institutions

apriorit.com

Medical

Health & Fitness

Information technology

Challenges in Manual Follicle Analysis

Medical professionals face significant time constraints and potential measurement inconsistencies due to manual analysis of ovarian follicles in ultrasound videos. The current workflow requires pausing videos to manually detect, segment, and measure follicles, leading to inefficiencies and potential human error in fertility treatment diagnostics.

About the Client

A healthcare institution specializing in infertility treatment and reproductive medicine requiring automated diagnostic tools

Automation and Accuracy Goals

Automate follicle detection and measurement in ultrasound videos
Achieve medical-grade accuracy (90%+ precision, 97%+ recall)
Reduce manual analysis time by 70% or more
Generate standardized measurement reports with analytics

Core System Capabilities

Real-time video frame analysis with follicle detection
Instance segmentation using Mask R-CNN architecture
Automated diameter/perimeter measurement with pixel-to-mm calibration
Hatch mark recognition for scale calibration
Multi-format video/image processing pipeline
Comprehensive analytics dashboard with precision metrics

Technical Implementation Approach

Mask R-CNN with ResNet backbone

TensorFlow/PyTorch frameworks

OpenCV for image processing

Google Colab/Azure ML environments

DICOM standard compliance

System Integration Requirements

Medical imaging equipment APIs
Electronic Health Record (EHR) systems
FDA-certified medical device workflows

Critical System Requirements

Medical-grade accuracy (90%+ precision)
HIPAA-compliant data security
Scalable cloud infrastructure
Real-time processing performance
Regulatory compliance (FDA Class II certification)

Transforming Fertility Treatment Workflow

Implementation of this AI system is expected to reduce manual analysis time by 80%, enable faster treatment decisions, and improve diagnostic consistency across clinics. The solution will support FDA-approved medical device certification while maintaining 97% recall rate for critical follicle detection, directly impacting patient outcomes through accelerated fertility treatment cycles.