Artificial Intelligence for Intraoperative Guidance: Using Semantic Segmentation to Identify Surgical Anatomy During Laparoscopic Cholecystectomy.

ABSTRACT:

Objective

The aim of this study was to develop and evaluate the performance of artificial intelligence (AI) models that can identify safe and dangerous zones of dissection, and anatomical landmarks during laparoscopic cholecystectomy (LC).

Summary background data

Many adverse events during surgery occur due to errors in visual perception and judgment leading to misinterpretation of anatomy. Deep learning, a subfield of AI, can potentially be used to provide real-time guidance intraoperatively.

Methods

Deep learning models were developed and trained to identify safe (Go) and dangerous (No-Go) zones of dissection, liver, gallbladder, and hepatocystic triangle during LC. Annotations were performed by 4 high-volume surgeons. AI predictions were evaluated using 10-fold cro

SUBMITTER: Madani A 

PROVIDER: S-EPMC8186165 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature