Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification
1
Zitationen
8
Autoren
2025
Jahr
Abstract
Minimally invasive surgery is complex and prone to variation not routinely objectively measured. We established an association between skills and patient outcomes. The evolving application of artificial intelligence techniques could assist intraoperative analysis. In this study, we analysed 77 rectal cancer operations' videos from a multicentre RCT that were recorded unedited and underwent blinded manual analysis using a validated, bespoke performance assessment tool (LapTMEpt) and the Objective Clinical Human Reliability Analysis (OCHRA). The OCHRA methodology involved segmentation of the 77 operations and manually annotating each case for the enacted errors and near misses. We provide a detailed description of the errors and near misses of over 380 hours of video analysis, containing 1377 errors. This dataset can inform machine learning to assist progress toward a fully automated, objective assessment of surgical skills.
Ähnliche Arbeiten
The SCARE 2020 Guideline: Updating Consensus Surgical CAse REport (SCARE) Guidelines
2020 · 5.573 Zit.
Virtual Reality Training Improves Operating Room Performance
2002 · 2.789 Zit.
An estimation of the global volume of surgery: a modelling strategy based on available data
2008 · 2.508 Zit.
Objective structured assessment of technical skill (OSATS) for surgical residents
1997 · 2.258 Zit.
Does Simulation-Based Medical Education With Deliberate Practice Yield Better Results Than Traditional Clinical Education? A Meta-Analytic Comparative Review of the Evidence
2011 · 1.709 Zit.