Artificial intelligence (AI) integrated with high-throughput assays offers a powerful route to accelerate discovery in relevant biological models. Functional cardiac imaging is a prime application, where deep learning (DL) and explainable AI (xAI) can overcome limitations of traditional phenotyping methods, such as manual analysis, subjective interpretation, and low scalability. In cardiovascular research, the zebrafish model is highly valuable due to its translational relevance and accessibility for high-throughput applications. Here, we present ZeCardioAI, a computational platform combining zebrafish experimental advantages with DL and xAI methodologies. The platform automatically extracts comprehensive cardiac phenotypes from live imaging, achieving high precision while maintaining interpretability, critical for mechanistic insight and translational validation. ZeCardioAI, when applied to zebrafish models of dilated and hypertrophic cardiomyopathy (CM), detected subtle yet clinically relevant phenotypic differences. Machine learning classifiers achieved robust separation of disease from healthy phenotypes, and xAI revealed discriminative features aligning with established clinical markers. Our developments should prove valuable in addressing the unmet medical need in CMs to find new, specific treatments. The platform's modular architecture supports future adaptation to diverse disease contexts beyond CMs, enabling large-scale, fully automated phenotyping at a throughput unattainable by manual approaches. ZeCardioAI establishes a new standard for AI-powered biological research, offering transformative potential for accelerating drug discovery, advancing precision medicine approaches, and deepening fundamental understanding of complex biological systems across multiple therapeutic areas.
Arqué et al. (Sun,) studied this question.