Why the study?
Although surgical workflow analysis achieved high precision in single-center data, the generalizability of phase recognition algorithms in a multi-center setting including more difficult tasks like surgical action and skill remained uninvestigated.
Population
33 laparoscopic cholecystectomy videos from three surgical centers
Comparison
12 teams machine learning algorithms for recognition of phase, action, instrument, or skill assessment
Design
Comparative validation benchmark challenge
Key result
Machine learning algorithms achieved F1-scores between 23.9% and 67.7% for surgical phase recognition, demonstrating that surgical workflow analysis is a promising but unsolved challenge.
Authors
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Single-center surgical phase recognition may aid workflow analysis; leaves open multi-center generalizability for clinical systems.
Machine learning algorithms for surgical workflow and skill analysis show promise but require further development, as demonstrated by variable performance in a multi-center benchmark challenge.
Wagner et al. (2021) studied Laparoscopic cholecystectomy (n=33). Machine learning algorithms was evaluated on F1-score for phase recognition. Machine learning algorithms achieved F1-scores between 23.9% and 67.7% for surgical phase recognition, demonstrating that surgical workflow analysis is a promising but unsolved challenge.