Exploring novel algorithms for atrial fibrillation detection by driving graduate level education in medical machine learning. (29th July 2022)

• Record Type: Journal Article
• Authors: Rohr, Maurice; Reich, Christoph; Höhl, Andreas; Lilienthal, Timm; Dege, Tizian; Plesinger, Filip; Bulkova, Veronika; Clifford, Gari; Reyna, Matthew; Hoog Antink, Christoph
• Is Part Of: Physiological measurement. Volume 43:Number 7(2022)
• Publication Date: 2022-07-29
• Subjects: gamification -- atrial fibrillation -- electrocardiogram -- deep learning; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/ac7840
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class 'Artificial Intelligence in Medicine Challenge', which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 'AF Classification from a Short Single Lead ECG Recording'. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1 scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge.