A Computer Vision Platform to Automatically Locate Critical Events in Surgical Videos: Documenting Safety in Laparoscopic Cholecystectomy.
Ann Surg
; 274(1): e93-e95, 2021 07 01.
Article
em En
| MEDLINE
| ID: mdl-33417329
ABSTRACT
OBJECTIVE:
The aim of this study was to develop a computer vision platform to automatically locate critical events in surgical videos and provide short video clips documenting the critical view of safety (CVS) in laparoscopic cholecystectomy (LC).BACKGROUND:
Intraoperative events are typically documented through operator-dictated reports that do not always translate the operative reality. Surgical videos provide complete information on surgical procedures, but the burden associated with storing and manually analyzing full-length videos has so far limited their effective use.METHODS:
A computer vision platform named EndoDigest was developed and used to analyze LC videos. The mean absolute error (MAE) of the platform in automatically locating the manually annotated time of the cystic duct division in full-length videos was assessed. The relevance of the automatically extracted short video clips was evaluated by calculating the percentage of video clips in which the CVS was assessable by surgeons.RESULTS:
A total of 155 LC videos were analyzed 55 of these videos were used to develop EndoDigest, whereas the remaining 100 were used to test it. The time of the cystic duct division was automatically located with a MAE of 62.8â±â130.4âseconds (1.95% of full-length video duration). CVS was assessable in 91% of the 2.5 minutes long video clips automatically extracted from the considered test procedures.CONCLUSIONS:
Deep learning models for workflow analysis can be used to reliably locate critical events in surgical videos and document CVS in LC. Further studies are needed to assess the clinical impact of surgical data science solutions for safer laparoscopic cholecystectomy.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Gravação em Vídeo
/
Processamento de Imagem Assistida por Computador
/
Colecistectomia Laparoscópica
/
Documentação
/
Melhoria de Qualidade
/
Segurança do Paciente
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Ann Surg
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
França