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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.\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/ae87aa","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T22:48:44Z","timestamp":1783550924000},"page":"045036","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised machine learning-assisted multimodal characterization of cardiomyocytes on a thin-film-transistor microelectrode array (TFT-MEA)"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8588-0345","authenticated-orcid":true,"given":"Xingzhuo","family":"Hu","sequence":"first","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8192-2810","authenticated-orcid":true,"given":"Junichi","family":"Sugita","sequence":"additional","affiliation":[{"name":"The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katsuhito","family":"Fujiu","sequence":"additional","affiliation":[{"name":"The University of Tokyo"},{"name":"The University of Tokyo"},{"name":"Institute of Science Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0067-6989","authenticated-orcid":true,"given":"Agn\u00e8s","family":"Tixier-Mita","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Toshiyoshi","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"bpexae87aabib1","author":"Organization W.H.: Cardiovascular diseases (CVDs)","year":"2023","type":"other"},{"key":"bpexae87aabib2","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1161\/CIRCULATIONAHA.106.180200","type":"journal-article","article-title":"Recommendations for the standardization and interpretation of the electrocardiogram: Part I","volume":"115","author":"Kligfield","year":"2007","journal-title":"Circulation"},{"key":"bpexae87aabib3","doi-asserted-by":"publisher","DOI":"10.3389\/fradi.2023.1294068","type":"journal-article","article-title":"Applications of AI in multi-modal imaging for cardiovascular disease","volume":"3","author":"Milosevic","year":"2023","journal-title":"Front. 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