Development of an open-source artificial intelligence (AI) anonymizer for electrocardiogram scans

The application of artificial intelligence (AI) in the medical field has seen a significant increase in popularity, particularly for its ability to accurately detect abnormalities across a range of diagnostic tests. The effectiveness and precision of AI models are highly contingent on the quality and diversity of the training data used in their development. In the present work, we have developed an open-source AI model designed to anonymize electrocardiogram (ECG) recordings. This model achieves anonymization by automatically detecting and extracting the waveform data. This tool can be used to prepare input data that in turn serve as input variables for training AI models specifically for cardiology applications. By ensuring that patient-identifying information is removed while retaining the essential waveform data. The present model facilitates the creation of robust, privacy-preserving datasets that can enhance the training and performance of AI in cardiology.