Embryo Evaluation Model Using Image and Text Inputs
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Solution Overview
Problem
Conventional embryo evaluation systems in in vitro fertilization are limited to image data input and cannot interact with users for personalized evaluation requests, such as implantation rate or grade, lacking the ability to handle textual data inputs.
Innovation Solution
A data processing device that acquires both microscopic image data and textual data from users to input into a data generation model, generating evaluation data based on user requests using a generative AI model like ChatGPT or Gemini, allowing for personalized embryo evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If only image data is used for embryo evaluation, then the system is simple to operate, but the evaluation cannot be personalized according to user requests
Solution Approach 1:
The patent introduces a natural language processing intermediary that mediates between the user's textual evaluation requests and the embryo image data. The NLP module interprets user requests (e.g., 'evaluate implantation potential') and translates them into appropriate evaluation parameters, allowing personalized evaluation without requiring users to directly configure complex technical parameters.
Solution Approach 2:
The evaluation system is designed to handle multiple types of input data (image data and textual request data) and perform multiple evaluation functions (implantation rate, grade, viability, etc.) through a unified processing framework. This multi-functional design enables the system to adapt to different user needs while maintaining a consistent interface.
2Measurement precision
If multiple types of data are processed, then evaluation accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing on both image data and textual request data before the main evaluation. Image preprocessing (segmentation, feature extraction) and text preprocessing (tokenization, intent recognition) are conducted in advance, so that when the actual evaluation is performed, the processing time is minimized while still achieving high accuracy.
Solution Approach 2:
The evaluation process is divided into independent modular segments: image data processing module, textual request processing module, feature fusion module, and evaluation output module. Each module processes its specific data type independently and efficiently, then combines results in the fusion module, reducing overall processing time while maintaining comprehensive evaluation accuracy.
Data Source
AI summary
A data processing device includes: an acquisition unit configured to acquire microscopic image data of an embryo obtained through in vitro fertilization and request textual data describing an evaluation request for the embryo captured in the microscopic image data; an evaluation unit configured to input the microscopic image data and the request textual data to a data generation model and acquire evaluation data for the embryo captured in the microscopic image data, in which the evaluation data is output from the data generation model and corresponds to the request textual data; and an output unit configured to output the evaluation data for the embryo.