mTIPs: Multimodal Time-Series Imaging and Prediction System for Early Differentiation of Retinal Organoids

Retinal organoids (ROs) hold immense promise for disease modeling and therapy, but their production is hindered by developmental heterogeneity, necessitating robust characterization and early prediction. However, the limited application of current methods to human stem cell-derived organoids reduces clinical relevance; the omission of 3D multimodal imaging leads to incomplete morphological assessment; and the lack of time-series analysis diminishes applicability. To address these gaps, we developed mTIPs (Multimodal Time Series Imaging and Prediction System), which enables automated, early-stage quality control of human embryonic stem cell (hESC)-derived ROs. Firstly, we present the first publicly available multimodal, time-series imaging dataset of hESC-derived retinal organoids, establishing a foundational resource with significant clinical relevance that was previously lacking. Furthermore, we highlight the advantages of 3D imaging, demonstrating that Optical Coherence Tomography (OCT) derived volumetric data offers unique insights into development and significantly enhances early predictive accuracy compared to traditional 2D bright-field microscopy. Notably, we developed a practical, unified model using a single time-encoding network that achieves an AUROC exceeding 0.8 at all time points, including as early as Day 6-significantly outperforming expert manual classification. mTIPs offers a scalable framework to standardize organoid production, enhancing the reliability of ROs for research and future clinical applications.