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A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development

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Soft Optical Sensor for Embryo Quality Evaluation Based on Multi-Focal Image Fusion and RAG-Enhanced Vision

Domas Jonaitis¹, Vidas Raudonis^1,2, Egle Drejeriene³

¹Automation Department, Kaunas University of Technology, 51367 Kaunas, Lithuania.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This study introduces a novel soft optical sensor for automated human embryo quality assessment in in vitro fertilization (IVF). The system enhances accuracy by fusing multi-focal images, improving upon traditional subjective methods.

Keywords:

RAG Vision Transformer data fusion deep learning embryo grading explainable AI soft sensor

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Area of Science:

Reproductive Biology
Medical Imaging
Computational Pathology

Background:

Human embryo quality assessment is crucial for in vitro fertilization (IVF) success.
Current manual grading methods are subjective and limited by microscopy depth-of-field.
Need for objective, high-precision tools for standardized embryo evaluation.

Purpose of the Study:

To develop a novel "soft optical sensor" for automated, high-precision human embryo quality assessment.
To integrate computational innovations for enhanced morphological detail reconstruction and classification.
To provide explainable clinical rationales grounded in established guidelines.

Main Methods:

Developed a soft optical sensor architecture integrating multi-focal image fusion (3D-aware reconstruction from Z-stacks).
Employed a retrieval-augmented generation (RAG) framework with a Swin Transformer for classification and explanation.
Validated the system on a large clinical dataset of 102,308 images.

Main Results:

Achieved a diagnostic accuracy of 94.11% for embryo quality assessment.
Demonstrated a 9.43% performance improvement over standard single-plane analysis by fusing multi-focal data.
RAG module generated natural language explanations aligned with ESHRE guidelines, reducing inter-observer variability.

Conclusions:

The soft optical sensor offers a robust, automated tool for standardized morphological assessment in IVF.
Multi-focal image fusion is critical for improving accuracy in embryo evaluation.
Prospective validation against live birth outcomes is necessary for full clinical adoption.