SarcNet: A Novel AI-Based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged HIPSC-CMS

Quantifying sarcomere structure organization in human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is crucial for understanding cardiac disease pathology, improving drug screening, and advancing regenerative medicine. Traditional methods, such as manual annotation and Fourier transform analysis, are labor-intensive, error-prone, and lack high-throughput capabilities. This paper proposes SarcNet, a novel deep learning-based framework that leverages cell images and integrates cell features to automatically evaluate the sarcomere structure of hiPSC-CMs from the onset of differentiation. The proposed framework is a cell-features concatenated and linear layers-added ResNet-18 module, to output a continuous score ranging from 1 to 5 that captures the level of sarcomere structural organization. SarcNet achieves a Spearman correlation of 0.831 with expert evaluations, demonstrating superior performance and an improvement of 7.5% over the previous approach, which uses linear regression. Our results also show a consistent pattern of increasing organization from day 18 to day 32 of differentiation, aligning with expert evaluations. This study contributes to the development of a new automated tool for quantifying sarcomere structural organization in hiPSC-CMs, advancing cardiac research.