IVF Drop Tracking Using Computer Vision for Witness-Free Transfer
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Solution Overview
Problem
In the in-vitro fertilization (IVF) process, maintaining accurate tracking of biological materials during embryo handling is challenging due to the manual and error-prone nature of transferring embryos between dishes and drops, necessitating a second witness for verification, which increases costs and complexity.
Innovation Solution
An automated tracking system using computer vision and machine learning to identify and assign unique identifiers to drops and dishes, providing real-time feedback to ensure accurate transfer of biological materials without the need for a second witness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking with paper records and witness verification is used, then accuracy of tracking can be maintained through human verification, but device complexity and operational costs increase due to requiring additional personnel
Solution Approach 1:
The patent replaces manual mechanical tracking methods (paper records, human verification) with an automated computer vision system using cameras, machine learning models, and image processing to detect and track embryos, biopsy samples, and associated items throughout the IVF workflow, eliminating the need for additional witness personnel
Solution Approach 2:
The system enables self-service tracking where the computer vision system automatically identifies, tracks, and verifies the location and status of biological materials without requiring human intervention for verification, with the system performing its own monitoring and record-keeping functions
2Ease of manufacture
If manual tracking with paper records is used, then implementation costs are lower, but productivity decreases due to time-consuming verification processes
Solution Approach 1:
The computer vision system operates continuously to track biological materials throughout the entire IVF process, providing uninterrupted monitoring and real-time updates, eliminating the intermittent and time-consuming nature of manual verification processes
Solution Approach 2:
The system provides real-time feedback through automated tracking and verification, immediately detecting and reporting the location and status of embryos and biopsy samples, enabling rapid response and decision-making compared to delayed manual record-keeping
3Productivity
If automated computer vision tracking is implemented, then productivity increases through streamlined operations, but device complexity increases due to advanced technology requirements
Solution Approach 1:
The complex tracking system is segmented into distinct functional modules: image capture cameras, machine learning models for identification, image processing algorithms, and database management, allowing each component to be optimized independently and simplifying overall system implementation and maintenance
Solution Approach 2:
The computer vision system performs multiple functions including tracking embryos, tracking biopsy samples, verifying locations, maintaining chain of custody, and generating reports, consolidating what would otherwise require multiple separate systems or manual processes into a single multi-functional platform
Data Source
AI summary
The present disclosure relates to a method performed by one or more computers for tracking a biological material of a subject during an in-vitro fertilization process. The method includes receiving, from a camera, an image of a dish having a visual characteristic and a drop disposed on the dish, the dish holding the biological material at a drop location. The method then includes processing the image of the dish, using a drop identification model, to identify the drop according to the visual characteristic. Further, the method includes assigning an identifier to the drop associated with the drop location, and recording the identifier of the drop associated with the drop location.