Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study

Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study

This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTim...

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Bibliographic Details
Main Authors: Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Computer Assisted Surgery
Subjects:
Deep learning
time series
CNN
regression
Online Access:https://www.tandfonline.com/doi/10.1080/24699322.2025.2466426
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https://www.tandfonline.com/doi/10.1080/24699322.2025.2466426

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