Improving Machine Learning Education during the COVID-Pandemic Using Past Computing in Cardiology Challenges

0555166 - ÚPT 2022 RIV US eng C - Conference Paper (international conference)
Rohr, M. - Plešinger, Filip - Bulková, V. - Antink, C. H.
Improving Machine Learning Education during the COVID-Pandemic Using Past Computing in Cardiology Challenges.
2021 Computing in Cardiology (CinC). Vol. 48. New York: IEEE, 2021, č. článku 55. ISBN 978-166547916-5. ISSN 2325-8861. E-ISSN 2325-887X.
[Computing in Cardiology 2021 /48./. Brno (CZ), 12.09.2021-15.09.2021]
Institutional support: RVO:68081731
Keywords : ECG * Machine-learning * Classification * Atrial Fibrillation
OECD category: Medical engineering
https://ieeexplore.ieee.org/document/9662955

During the COVID-Pandemic and the lockdown of universities, the need for stimulating, novel teaching methods was high, as most students were confined to their homes. For over 20 years, the annual PhysioNet / CinC Challenges not only lead to technological advances for specific problems, they have also proven repeatedly to be of immense value from an educational point of view. In this paper, we report results from the class 'Artificial Intelligence in Medicine Challenge', which was implemented as an online project seminar at TU Darmstadt and which was heavily inspired by the PhysioNet / CinC Challenge 2017 'AF Classification from a Short Single Lead ECG Recording'. In particular, we show numeric results of the developed approaches on several datasets, highlight themes commonly observed among participants, and report the results from student evaluation. Several teams were able to implement approaches based on state-of-the-art algorithms achieving F1 scores above / close to 90 % on a hidden test-set of Holter recordings. Moreover, the self-assessment of the students reported a notable increase in machine learning knowledge.
Permanent Link: http://hdl.handle.net/11104/0329696