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BeatVecNet, an interpretable deep\u2010learning framework, classifies drug\u2010induced contractile phenotypes from beat\u2010gated motion vectors in human induced pluripotent stem cell\u2010derived cardiomyocytes (hiPSC\u2010CMs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Experimental Approach<\/jats:title>\n                    <jats:p>BeatVecNet was developed and evaluated using beat\u2010gated motion\u2010vector tensors derived from hiPSC\u2010CMs recorded by label\u2010free phase\u2010contrast imaging at 150\u2009fps. hiPSC\u2010CMs were cultured on micropatterned substrates to provide a consistent contraction axis for directional motion analysis. Dense optical flow was used to derive motion vectors, which were retained as beat\u2010gated spatiotemporal tensors rather than reduced to scalar traces. The model uses two convolutional branches to process horizontal and vertical velocity components separately and was evaluated on independent assays containing control, doxorubicin, trastuzumab and sunitinib.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Results<\/jats:title>\n                    <jats:p>BeatVecNet distinguished drug\u2010induced contractile phenotypes with a macro\u2010F1 of 0.916, outperforming conventional kinematic classifiers (best baseline 0.706), with the largest advantage at low doses. Branch\u2010replacement ablation indicated non\u2010redundant contributions of horizontal and vertical motion components, with trastuzumab classification showing greater sensitivity to horizontal\u2010component replacement and sunitinib classification to vertical\u2010component replacement. Phase\u2010resolved attribution further identified temporal patterns within the beat cycle not explained by peak velocity alone. Micropatterning improved signal consistency and contractile coherence, enabling robust directional motion analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion and Implications<\/jats:title>\n                    <jats:p>BeatVecNet extracts drug\u2010associated contractile signatures not captured by conventional scalar analysis while providing direction\u2010 and phase\u2010resolved interpretability at the motion\u2010representation level. This platform complements electrophysiology\u2010centred safety assessment and provides proof of concept for classification of subtle drug\u2010induced contractile phenotypes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bph.70710","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:24:55Z","timestamp":1791897895000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BeatVecNet classifies drug\u2010induced contractile phenotypes from beat\u2010gated motion vectors in micropatterned human induced pluripotent stem cell\u2010derived cardiomyocytes"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-5203","authenticated-orcid":false,"given":"Shigeyuki","family":"Magi","sequence":"first","affiliation":[{"name":"Division of Cell Physiology, Department of Physiology, School of Medicine Toho University  Tokyo 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