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

Annals of Surgery
Amin MadaniAdnan Alseidi

Abstract

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). 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. 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 four high-volume surgeons. AI predictions were evaluated using 10-fold cross-validation against annotations by expert surgeons. Primary outcomes were intersection-over-union (IOU) and F1 score (validated spatial correlation indices), and secondary outcomes were pixel-wise accuracy, sensitivity, specificity, ± standard deviation. AI models were trained on 2627 random frames from 290 LC videos, procured from 37 countries, 136 institutions and 153 surgeons. Mean IOU, F1 score, accuracy, sensitivity, and specificity for the AI to identify Go zones were 0.53 (±0.24), 0.70 (±0.28), 0.94 (±0.05), 0.69 (±0.20...Continue Reading

Citations

Jun 15, 2021·Computer Assisted Surgery·Thomas M WardDaniel A Hashimoto
Jul 14, 2021·Scientific Reports·Julia GongSerena Yeung
Sep 25, 2021·Surgery Today·Takashi SakamotoAlan Kawarai Lefor

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