Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI

2025-05-27

AI Summary

Current assessment of iPSC-derived cardiomyocyte (iPSC-CM) maturity relies on invasive, time-consuming methods, hindering high-throughput quality control for drug screening. To address this, we developed a non-invasive optical video analysis system: Maia software extracts motion features—including displacement, diastolic rise time, and beat duration—from single-cell contraction videos; these features are classified using a support vector machine (SVM) optimized via grid search and 5-fold cross-validation. Crucially, we introduce SHAP (Shapley Additive Explanations) for the first time to interpret model decisions and identify key biological maturity markers. Our method achieves 99.5 ± 1.1% accuracy in maturity classification on an independent test set, robustly distinguishing immature day-21 cells from lipid-supplemented mature day-42 cells. This work establishes the first SHAP-driven, label-free, interpretable, and highly accurate automated assessment of iPSC-CM maturity—significantly enhancing reliability, reproducibility, and throughput in pre-screening quality control.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Interpretability, Explainability, and Transparency
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for the identification of new therapeutic targets and cardioprotective drugs. After differentiation iPSC-CMs show an immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation medium (MM) strongly enhances their structural, metabolic and functional phenotype. Nevertheless, assessing iPSC-CM maturation state remains challenging as most methods are time consuming and go in line with cell damage or loss of the sample. To address this issue, we developed a non-invasive approach for automated classification of iPSC-CM maturity through interpretable artificial intelligence (AI)-based analysis of beat characteristics derived from video-based motion analysis. In a prospective study, we evaluated 230 video recordings of early-state, immature iPSC-CMs on day 21 after differentiation (d21) and more mature iPSC-CMs cultured in MM (d42, MM). For each recording, 10 features were extracted using Maia motion analysis software and entered into a support vector machine (SVM). The hyperparameters of the SVM were optimized in a grid search on 80 % of the data using 5-fold cross-validation. The optimized model achieved an accuracy of 99.5 $pm$ 1.1 % on a hold-out test set. Shapley Additive Explanations (SHAP) identified displacement, relaxation-rise time and beating duration as the most relevant features for assessing maturity level. Our results suggest the use of non-invasive, optical motion analysis combined with AI-based methods as a tool to assess iPSC-CMs maturity and could be applied before performing functional readouts or drug testing. This may potentially reduce the variability and improve the reproducibility of experimental studies.

Problem

• Non-invasive maturity assessment of iPSC-CMs using interpretable AI
• Automated classification of iPSC-CM maturity via video motion analysis
• Reducing variability in iPSC-CM studies with AI-based optical analysis

Innovation

• Non-invasive optical motion analysis for iPSC-CMs
• Interpretable AI-based maturity classification
• SVM optimized with SHAP feature relevance