Non-Invasive Embryo Viability Tracking via Hypothesis Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing human embryo viability in IVF are invasive, unreliable, and fail to accurately predict implantation potential due to noisy biological images and errors in automated cell tracking, leading to erroneous decisions in embryo selection for transfer.
Innovation Solution
An apparatus and method for automated, non-invasive cell activity tracking using a hypothesis selection module to determine inferred characteristics of cells based on geometric features, with a confidence estimation module to assess the reliability of these hypotheses, allowing for accurate evaluation of embryo viability without invasive methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated cell tracking is used to assess embryo viability, then productivity is improved, but measurement precision deteriorates due to noisy biological images and tracking errors
Solution Approach 1:
The system implements feedback mechanisms where tracking results are continuously refined. The hypothesis selection module uses feedback from image data and previous tracking results to adjust and improve cell position and boundary estimates, thereby maintaining measurement precision while achieving automated high-throughput assessment
Solution Approach 2:
The patent introduces an intermediary hypothesis selection module that acts as a mediator between raw image data and final viability assessment. This module generates multiple hypotheses about cell states and selects the most probable ones, effectively filtering noise and tracking errors to maintain precision while enabling automated processing
2Measurement precision
If invasive methods are used to assess embryo quality, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent replaces invasive mechanical assessment methods with non-invasive optical imaging and computational analysis. Time-lapse microscopy captures embryo development without physical contact, and automated image analysis algorithms assess viability based on morphological and temporal patterns, eliminating physical disruption while maintaining assessment accuracy
Solution Approach 2:
The hypothesis selection module serves as an intermediary that extracts viability information from non-invasive time-lapse images. By analyzing temporal patterns of cell division and morphological changes across multiple time points, the system achieves accurate quality assessment without direct intervention or disruption to the embryo
Data Source
Figure 1A
Figure 1B
Figure 2A
AI summary
Apparatuses, methods, and systems for automated, non-invasive evaluation of cell activity are provided. In one embodiment, an apparatus includes a hypothesis selection module configured to select a hypothesis from a plurality of hypotheses characterizing one or more cells shown in an image. Each of the plurality of hypotheses includes an inferred characteristic of the one or more cells based on geometric features of the one or more cells shown in the image. The hypothesis selection module is implemented in at least one of a memory or a processing device.