Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study.
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| Title: | Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study. |
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| Authors: | Frassini E; Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands., Vijfvinkel TS; Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.; Reinier de Graaf Hospital, Delft, The Netherlands., Butler RM; Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands., van der Elst M; Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.; Reinier de Graaf Hospital, Delft, The Netherlands., Hendriks BHW; Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.; Medical Systems, Philips Medical Systems, Best, The Netherlands., van den Dobbelsteen JJ; Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands. |
| Source: | Computer assisted surgery (Abingdon, England) [Comput Assist Surg (Abingdon)] 2025 Dec; Vol. 30 (1), pp. 2466426. Date of Electronic Publication: 2025 Feb 24. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Taylor & Francis Country of Publication: England NLM ID: 101681550 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2469-9322 (Electronic) Linking ISSN: 24699322 NLM ISO Abbreviation: Comput Assist Surg (Abingdon) Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2469-9322 |
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| DOI: | 10.1080/24699322.2025.2466426 |
