Unsupervised machine learning-assisted multimodal characterization of cardiomyocytes on a thin-film-transistor microelectrode array (TFT-MEA)

cardiac tissue analysis. We present a machine learning (ML)-assisted framework for multimodal characterization of cardiomyocyte activity recorded using a transparent thin-film-transistor MEA, allowing simultaneous acquisition of electrophysiological and optical data. The features of both modalities were integrated, and unsupervised learning identified contraction patterns without prior labels. Reliability was assessed with supervised models and SHapley Additive Explanations to confirm feature importance and label consistency. Pharmacological validation with isoprenaline showed the expected increase in beating frequency, detected automatically by our pipeline. Multimodal feature correlations with emergent labels demonstrated that interpretable ML can extract biologically meaningful insights from complex datasets. While isoprenaline served as a proof-of-concept, the framework is applicable for detecting diverse drug- or disease-induced cardiac network changes. This approach enables scalable, label-free and automated assessment of cardiomyocytes networks for drug discovery, toxicity screening, and mechanistic studies.