用视频分析+可解释AI，无损判断心脏细胞成熟度。
Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI
- 通过视频捕捉细胞跳动，提取10个运动特征，用SVM分类成熟度。
- 模型准确率达99.5%，在230组数据上表现稳定。
- 识别出位移、舒张上升时间和搏动时长为关键指标，适合药效研究前筛查。
人源诱导多能干细胞来源的心肌细胞（iPSC-CMs）是发现新治疗靶点和心保护药物的重要资源。分化后iPSC-CMs呈未成熟胎儿样表型。在添加脂质的成熟培养基（MM）中培养可显著提升其结构、代谢与功能表型。然而，评估其成熟度仍具挑战，因多数方法耗时且损伤细胞。为此，我们开发了一种非侵入性自动化方法：基于视频运动分析提取搏动特征，结合可解释人工智能（AI）进行成熟度分类。在前瞻性研究中，分析了230个视频记录，涵盖分化第21天（d21）的早期未成熟iPSC-CMs，以及在MM中培养至第42天（d42, MM）的更成熟细胞。每段视频提取10个特征，输入支持向量机（SVM），通过网格搜索与5折交叉验证优化超参数。优化后模型在保留测试集上准确率达99.5 ± 1.1%。Shapley Additive Explanations（SHAP）识别出位移、舒张上升时间与搏动时长为最关键特征。结果表明，该光学非侵入方法结合AI可用于提前评估iPSC-CMs成熟度，适用于功能检测或药物测试前筛选，有助于降低实验变异性，提升可重复性。
原文摘要 · Abstract (English)
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.
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