{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["27"],"submitter":["Scheurer F"],"pubmed_abstract":["Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for identifying novel therapeutic targets and cardioprotective drugs. However, a key limitation of iPSC-CMs is their immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation media (MM) enhances the structural, metabolic and electrophysiological properties of iPSC-CMs. Nevertheless, they face substantial limitations as there are labor-intensive, time consuming and go in line with cell damage or loss of the sample. To address this issue, we have 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 pros"],"journal":["Computational and structural biotechnology journal"],"pagination":["3719-3728"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12409467"],"repository":["biostudies-literature"],"pubmed_title":["Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI."],"pmcid":["PMC12409467"],"pubmed_authors":["Guan K","Sonntag F","Schmidt M","Schubert M","Hammer A","Steiner RP","Gamm O","Scheurer F","Malberg H"],"additional_accession":[]},"is_claimable":false,"name":"Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI.","description":"Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are an important resource for identifying novel therapeutic targets and cardioprotective drugs. However, a key limitation of iPSC-CMs is their immature, fetal-like phenotype. Cultivation of iPSC-CMs in lipid-supplemented maturation media (MM) enhances the structural, metabolic and electrophysiological properties of iPSC-CMs. Nevertheless, they face substantial limitations as there are labor-intensive, time consuming and go in line with cell damage or loss of the sample. To address this issue, we have 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 pros","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-03T07:06:18.635Z","creation":"2026-05-29T03:05:29.96Z"},"accession":"S-EPMC12409467","cross_references":{"pubmed":["40917985"],"doi":["10.1016/j.csbj.2025.08.024"]}}