Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Navigation/Suche
- Forscher*innen
- Institutionen
- Projekte
- Publikationen
- Partner
- Geldgeber
- Ausstattung
- Knowhow
- Forschungsfelder
"Köpfe" der aktuellen Meldungen:
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Isaksen, JL; Norregaard, M; Manninger, M; Dobrev, D; Jespersen, T; Hermans, B; Heijman, J; Plank, G; Scherr, D; Pock, T; Thambawita, V; Riegler, MA; Kanters, JK; Linz, D.
Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice
IJC HEART VASC. 2025; 60: 101783
Doi: 10.1016/j.ijcha.2025.101783
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-Autor*innen der Med Uni Graz
- Heijman Jordi
- Manninger-Wünscher Martin
- Plank Gernot
- Scherr Daniel
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- Machine learning methods are increasingly used in cardiovascular research. In order to highlight opportunities and challenges of the evaluation of studies applying machine learning, we use examples from cardiac electrophysiology, a field characterized by large and often imbalanced amounts of data. We provide recommendations and guidance on evaluating and presenting supervised machine learning studies. We recommend proper cohort selection, keeping training and testing data strictly separate, and comparing results to a reference model without machine learning as basic principles to ensure the quality of studies using machine learning methods. We furthermore recommend specific metrics and plots when reporting on machine learning including on models for multi-channel time series or images. This Best Practice paper represents a possible blueprint to help evaluate machine learning-based medical tests in cardiac electrophysiology and beyond.
- Find related publications in this database (Keywords)
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Medical Test
- Evaluation