Automation of surgical skill assessment using a three-stage machine learning algorithm

Lavanchy, Joël L.; Zindel, Joel; Kirtac, Kadir; Twick, Isabell; Hosgor, Enes; Candinas, Daniel; Beldi, Guido (2021). Automation of surgical skill assessment using a three-stage machine learning algorithm. Scientific reports, 11(1) Springer Nature 10.1038/s41598-021-84295-6

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Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Visceral Surgery and Medicine > Visceral Surgery

UniBE Contributor:

Lavanchy, Joël Lukas; Zindel, Joel; Candinas, Daniel and Beldi, Guido

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2045-2322

Publisher:

Springer Nature

Language:

English

Submitter:

Joël Lukas Lavanchy

Date Deposited:

08 Apr 2021 16:41

Last Modified:

11 Apr 2021 03:07

Publisher DOI:

10.1038/s41598-021-84295-6

BORIS DOI:

10.7892/boris.153605

URI:

https://boris.unibe.ch/id/eprint/153605

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