Division of Gastroenterological and General Surgery, Department of Surgery, School of Medicine, Showa University, Tokyo, Japan.
Division of Gastroenterological and General Surgery, Department of Surgery, School of Medicine, Showa University, Tokyo, Japan;
Anticancer Res. 2023 Nov;43(11):5235-5243. doi: 10.21873/anticanres.16725.
BACKGROUND/AIM: Laparoscopic hepatectomy (LH) requires accurate visualization and appropriate handling of hepatic veins and the Glissonean pedicle that suddenly appear during liver dissection. Failure to recognize these structures can cause injury, resulting in severe bleeding and bile leakage. This study aimed to develop a novel artificial intelligence (AI) system that assists in the visual recognition and color presentation of tubular structures to correct the recognition gap among surgeons.
Annotations were performed on over 350 video frames capturing LH, after which a deep learning model was developed. The performance of the AI was evaluated quantitatively using intersection over union (IoU) and Dice coefficients, as well as qualitatively using a two-item questionnaire on sensitivity and misrecognition completed by 10 hepatobiliary surgeons. The usefulness of AI in medical education was qualitatively evaluated by 10 medical students and residents.
The AI model was able to individually recognize and colorize hepatic veins and the Glissonean pedicle in real time. The IoU and Dice coefficients were 0.42 and 0.53, respectively. Surgeons provided a mean sensitivity score of 4.24±0.89 (from 1 to 5; Excellent) and a mean misrecognition score of 0.12±0.33 (from 0 to 4; Fail). Medical students and residents assessed the AI to be very useful (mean usefulness score, 1.86±0.35; from 0 to 2; Excellent).
The novel AI presented was able to assist surgeons in the intraoperative recognition of microstructures and address the recognition gap among surgeons to ensure a safer and more accurate LH.
背景/目的：腹腔镜肝切除术 (LH) 需要准确地观察和处理肝静脉和在肝解剖过程中突然出现的 Glisson 蒂。未能识别这些结构可能会导致损伤，从而导致严重出血和胆汁泄漏。本研究旨在开发一种新的人工智能 (AI) 系统，以协助识别管状结构并呈现颜色，以纠正外科医生之间的识别差距。
对超过 350 个捕捉 LH 的视频帧进行注释，然后开发一个深度学习模型。使用交并比 (IoU) 和骰子系数对 AI 的性能进行定量评估，并用 10 名肝胆外科医生完成的关于敏感性和误识别的两项问卷调查进行定性评估。10 名医学生和住院医师对 AI 在医学教育中的有用性进行了定性评估。
AI 模型能够实时识别和着色肝静脉和 Glisson 蒂。IoU 和骰子系数分别为 0.42 和 0.53。外科医生的平均敏感性评分为 4.24±0.89（1 到 5；优秀），平均误识别评分为 0.12±0.33（0 到 4；失败）。医学生和住院医师认为 AI 非常有用（平均有用性评分，1.86±0.35；0 到 2；优秀）。
所提出的新型 AI 能够协助外科医生在手术中识别微观结构，并解决外科医生之间的识别差距，以确保更安全、更准确的 LH。
