# Prospective Assessment of Embryoid Body by Deep Learning on Label-Free Time-Lapse Images from the Microwell Array

## TL;DR

This study uses deep learning on time-lapse images to predict the formation and size of embryoid bodies early in the process, improving high-throughput organoid manufacturing.

## Contribution

A novel deep learning framework for prospective, label-free prediction of embryoid body formation and size using early-phase imaging.

## Key findings

- The classification model achieved 96.5% accuracy in predicting successful EB formation.
- The regression model predicted final EB diameter with a mean absolute error of ±7.1 µm.
- The framework captures seeding-density-dependent size variations and supports automation in organoid culture.

## Abstract

Background: Embryoid bodies (EBs) play a central role in organoid engineering, where their formation fidelity and size critically influence downstream differentiation outcomes. Current EB production workflows primarily rely on retrospective quality assessment, which limits reproducibility in high-throughput culture systems. Objective: This study aimed to develop a prospective, non-invasive framework that integrates early-phase bright-field time-lapse imaging with a three-dimensional convolutional neural network to predict EB formation outcomes and final EB diameter within the microwell platform. Methods: Time-lapse image sequences collected during the first hours after cell seeding on the microwell array were used to train 3D-CNN models for classification (formation vs. non-formation) and regression (final diameter). A balanced dataset was constructed through under-sampling, and five-fold cross-validation with data augmentation was applied to evaluate model performance. Results: The classification model achieved an accuracy of 96.5%, reliably distinguishing between successful and failed EB formation using short-duration image sequences. The regression model predicted the final EB diameter with a mean absolute error of ±7.1 µm, reflecting strong agreement with measured values and capturing seeding-density-dependent size variations. Conclusions: Early aggregation dynamics captured by bright-field time-lapse imaging contain sufficient spatiotemporal information to enable accurate, prospective EB quality prediction. The proposed framework provides a label-free and automation-compatible strategy for improving reproducibility in large-scale EB manufacturing and supports the future development of adaptive and closed-loop organoid culture systems for clinical applications.