A Deep Learning Pipeline Using Synthetic Data to Improve Interpretation of Paper ECG Images

Cardiovascular diseases (CVDs) are the leading global cause of death, and early detection is essential to improve patient outcomes. Electrocardiograms (ECGs), especially 12-lead ECGs, play a key role in the identification of CVDs. These are routinely interpreted by human experts, a process that is time-consuming and requires expert knowledge. Historical research in this area has focused on automatic ECG interpretation from digital signals, with recent deep learning approaches achieving strong results. In practice, however, most ECG data in clinical practice are stored or shared in image form. To bridge this gap, we propose a deep learning framework designed specifically to classify paper-like ECG images into five main diagnostic categories. Our method was the winning entry to the 2024 British Heart Foundation Open Data Science Challenge. It addresses two main challenges of paper ECG classification: visual noise (e.g., shadows or creases) and the need to detect fine-detailed waveform patterns. We propose a pre-processing pipeline that reduces visual noise and a two-stage fine-tuning strategy: the model is first fine-tuned on synthetic and external ECG image datasets to learn domain-specific features, and then further fine-tuned on the target dataset to enhance disease-specific recognition. We adopt the ConvNeXt architecture as the backbone of our model. Our method achieved AUROC scores of 0.9688 on the public validation set and 0.9677 on the private test set of the British Heart Foundation Open Data Science Challenge, highlighting its potential as a practical tool for automated ECG interpretation in clinical workflows.

Empirical investigation of multi-source cross-validation in clinical machine learning

Traditionally, machine learning-based clinical prediction models have been trained and evaluated on patient data from a single source, such as a hospital. Cross-validation methods can be used to estimate the accuracy of such models on new patients originating from the same source, by repeated random splitting of the data. However, such estimates tend to be highly overoptimistic when compared to accuracy obtained from deploying models to sources not represented in the dataset, such as a new hospital. The increasing availability of multi-source medical datasets provides new opportunities for obtaining more comprehensive and realistic evaluations of expected accuracy through source-level cross-validation designs. In this study, we present a systematic empirical evaluation of standard K-fold cross-validation and leave-source-out cross-validation methods in a multi-source setting. We consider the task of electrocardiogram based cardiovascular disease classification, combining and harmonizing the openly available PhysioNet CinC Challenge 2021 and the Shandong Provincial Hospital datasets for our study. Our results show that K-fold cross-validation, both on single-source and multi-source data, systemically overestimates prediction performance when the end goal is to generalize to new sources. Leave-source-out cross-validation provides more reliable performance estimates, having close to zero bias though larger variability. The evaluation highlights the dangers of obtaining misleading cross-validation results on medical data and demonstrates how these issues can be mitigated when having access to multi-source data.