标题： 基于光学成熟特征的iPSC-CMs无创成熟评估使用可解释AI 显示英文标题

标题： Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI

摘要： 诱导多能干细胞衍生的心肌细胞（iPSC-CMs）是识别新的治疗靶点和心脏保护药物的重要资源。 分化后的iPSC-CMs表现出未成熟、类似胎儿的表型。 在补充脂质的成熟培养基（MM）中培养iPSC-CMs可以显著增强其结构、代谢和功能表型。 然而，评估iPSC-CM的成熟状态仍然具有挑战性，因为大多数方法耗时且伴随着细胞损伤或样本丢失。 为了解决这个问题，我们开发了一种非侵入性的方法，通过基于视频运动分析提取的节律特征的可解释人工智能（AI）分析，实现iPSC-CM成熟度的自动化分类。 在一项前瞻性研究中，我们在分化后第21天（d21）评估了早期、未成熟的iPSC-CMs的230个视频记录，以及在MM中培养的更成熟的iPSC-CMs（d42, MM）。 对于每个记录，使用Maia运动分析软件提取了10个特征，并输入到支持向量机（SVM）中。 SVM的超参数通过5倍交叉验证在数据的80%上进行网格搜索优化。 优化模型在留出测试集上的准确率为99.5 $\pm$ 1.1%。 Shapley 加性解释（SHAP）确定位移、松弛上升时间和节律持续时间作为评估成熟度水平的最相关特征。 我们的结果表明，非侵入性光学运动分析结合基于AI的方法可用作评估iPSC-CMs成熟度的工具，并可在进行功能读出或药物测试之前应用。 这可能有助于减少实验研究中的变异性并提高其重复性。 显示英文摘要

摘要： 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.