Advances in Artificial Intelligence-Enhanced Electrocardiography: A Pathway towards Improved Diagnosis and Patient Care

Vijay S Chauhan¹Remi Dubois²Domenico L Gatti^3*Jichao Zhao⁴

• ¹Peter Munk Cardiac Centre, University Health Network (UHN), Toronto, Canada
• ²IHU LIRYC: Cardiac Electrophysiology and Heart Modeling Institute, Bordeaux, France
• ³School of Medicine, Wayne State University, Detroit, United States
• ⁴The University of Auckland, Auckland, New Zealand

proposing a camera-based approach to ECG electrode localization. Using 2D and 3D computer vision algorithms, the authors demonstrate sub-centimeter accuracy in reconstructing electrode positions on the human torso. The study's reliance on off-the-shelf cameras and open-source algorithms makes it a scalable solution for BSPM expansion. Kim et al. [7] focus on scanned or imaged ECG paper strips, which are still widely used in many healthcare settings. Their two-stage deep learning system combines a Faster R-CNN for detecting ST-segment elevation with an ensemble model for infarction territory classification. This paper underscores a growing priority in AI research: meeting clinicians where they are. By enabling analysis of ECG images (rather than raw digital signals), the model is inherently compatible with a wide array of existing workflows, including those in resource-constrained environments, and is particularly attractive for frontline decision support. Neural network performance depends critically on the quality and relevance of input data. Ramirez et al. [8] explore this issue by applying a mutual information analysis to ECG leads, identifying redundant information across the 12 standard leads and testing various reduced-lead configurations. Their results reveal that a well-selected 6-lead subset, and vectorcardiographic transformations, can match or exceed full 12-lead performance, and preserve classification accuracy while reducing computational load. This has significant implications for wearable devices and mobile health, where data acquisition may be limited. By optimizing for both informativeness and parsimony, this work advances the efficiency and scalability of AI-driven ECG diagnostics. QRS detection is a fundamental building block in any ECG analysis pipeline. Zhao et al. [9] contribute a compact, yet highly accurate DNN model based on feature pyramid networks and dual-channel input. The model's minimal size (∼27k parameters) and fast inference make it ideal for edge computing applications, such as smartwatches, fitness trackers, or implantable devices. Zhao et al.'s work reinforces the emerging consensus that the future of AI-enhanced ECG lies in small, explainable, and highly optimized models tailored to specific tasks within broader clinical systems. Extending the reach of AI-enhanced ECG into maternal-fetal medicine, Wahbah et al. [10] present a bi-directional LSTM-based framework for extracting fetal ECG (fECG) signals from abdominal recordings. Their model achieves high accuracy and demonstrates resilience even during stages where the fetal signal is physiologically obscured. As fetal and neonatal ECGs pose unique signal processing challenges, this study opens new avenues for AI-assisted perinatal care, remote monitoring, and early detection of congenital abnormalities. Looking Forward: A Field Poised for Impact The contributions to this Research Topic highlight a discipline on the cusp of transformation. From novel signal processing and intelligent hardware to regulatory-aware, interpretable algorithms, the field of AI-enhanced ECG analysis is advancing rapidly toward real-world impact. However, critical challenges remain. Generalizability across diverse populations, integration with electronic health records (EHRs), and validation in prospective trials are essential next steps. Ethical considerations, especially around algorithmic bias, data privacy, and clinical accountability, must be integrated into development from the outset. We are beginning to see new frontiers: multimodal integration (combining ECG with imaging, labs, or genomics), personalized risk prediction, and AI-guided therapeutic interventions. As Editors of this Research Topic, we are inspired by the diversity, creativity, and clinical awareness shown by the authors in this issue. The articles not only advance the science of AI in ECG analysis but also illuminate the path to meaningful clinical translation. Together, their work illustrates a maturing ecosystem of tools, methods, and philosophies ready to shape the next era of cardiovascular care.