The development of a deep learning model for automated segmentation of the robotic pancreaticojejunostomy.
Surg Endosc
; 38(5): 2553-2561, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38488870
ABSTRACT
BACKGROUND:
Minimally invasive surgery provides an unprecedented opportunity to review video for assessing surgical performance. Surgical video analysis is time-consuming and expensive. Deep learning provides an alternative for analysis. Robotic pancreaticoduodenectomy (RPD) is a complex and morbid operation. Surgeon technical performance of pancreaticojejunostomy (PJ) has been associated with postoperative pancreatic fistula. In this work, we aimed to utilize deep learning to automatically segment PJ RPD videos.METHODS:
This was a retrospective review of prospectively collected videos from 2011 to 2022 that were in libraries at tertiary referral centers, including 111 PJ videos. Each frame of a robotic PJ video was categorized based on 6 tasks. A 3D convolutional neural network was trained for frame-level visual feature extraction and classification. All the videos were manually annotated for the start and end of each task.RESULTS:
Of the 100 videos assessed, 60 videos were used for the training the model, 10 for hyperparameter optimization, and 30 for the testing of performance. All the frames were extracted (6 frames/second) and annotated. The accuracy and mean per-class F1 scores were 88.01% and 85.34% for tasks.CONCLUSION:
The deep learning model performed well for automated segmentation of PJ videos. Future work will focus on skills assessment and outcome prediction.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pancreaticojejunostomia
/
Procedimentos Cirúrgicos Robóticos
/
Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
Surg Endosc
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
GASTROENTEROLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos