Abstract：
The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.
摘要：
药物引起的心脏毒性的鉴定仍然是一个具有深远的临床和经济影响的紧迫挑战。经常导致患者伤害和资源密集型药物召回。目前的方法，包括体内和体外模型，在准确鉴定心脏毒性物质方面有严重的局限性。开创了这些传统技术的范式转变，我们的研究提出了两个基于深度学习的框架，STFT-CNN和SST-CNN,以显著提高的准确性和可靠性评估心脏毒性。利用诱导多能干细胞衍生的心肌细胞（iPSC-CM）的力量作为更人类相关的细胞模型，我们通过阻抗测量记录机械跳动信号。这些时间信号通过先进的变换技术转换成丰富的二维表示，特别是短时傅里叶变换(STFT)和同步压缩变换(SST)。这些转换后的数据被输入到拟议的框架中进行综合分析，包括药物类型分类，浓度分类,心脏毒性分类，和新药鉴定。与递归神经网络(RNN)和一维卷积神经网络(1D-CNN)等传统模型相比，SST-CNN在药物类型分类方面提供了98.55％的令人印象深刻的测试准确性，在区分心脏毒性和非心脏毒性药物方面提供了99％的准确性。它的可行性进一步强调了恒星的98.5％的平均精度用于各种浓度的分类，我们提出的框架的优越性强调了它们在彻底改变药物安全性评估方面的前景。具有可扩展性的潜力，它们代表了药物安全性评估的重大飞跃，提供了一条通往更强大的道路，高效,和人类相关的心脏毒性评估。
