Deep learning prediction of error and skill in robotic prostatectomy suturing.

ABSTRACT:

Background

Manual objective assessment of skill and errors in minimally invasive surgery have been validated with correlation to surgical expertise and patient outcomes. However, assessment and error annotation can be subjective and are time-consuming processes, often precluding their use. Recent years have seen the development of artificial intelligence models to work towards automating the process to allow reduction of errors and truly objective assessment. This study aimed to validate surgical skill rating and error annotations in suturing gestures to inform the development and evaluation of AI models.

Methods

SAR-RARP50 open data set was blindly, independently annotated at the gesture level in Robotic-Assisted Radical Prostatectomy (RARP) suturing. Manual objective asses

SUBMITTER: Sirajudeen N 

PROVIDER: S-EPMC11614916 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature