Towards determining clinical factors influencing critical structure identification using Artificial Intelligence.

IF 2.7 3区医学 Q2 GASTROENTEROLOGY & HEPATOLOGY

Hpb Pub Date : 2025-03-25 DOI:10.1016/j.hpb.2025.03.452

Isaac Tranter-Entwistle, Lucy Culshaw, Roma Vichhi, Yiu Luke, Carole Addis, Imanol Luengo, Maria Grammatikopoulou, Karen Kerr, Danail Stoyanov, Tim Eglinton, Saxon Connor

{"title":"Towards determining clinical factors influencing critical structure identification using Artificial Intelligence.","authors":"Isaac Tranter-Entwistle, Lucy Culshaw, Roma Vichhi, Yiu Luke, Carole Addis, Imanol Luengo, Maria Grammatikopoulou, Karen Kerr, Danail Stoyanov, Tim Eglinton, Saxon Connor","doi":"10.1016/j.hpb.2025.03.452","DOIUrl":null,"url":null,"abstract":"Background: Studys into factors influencing critical view of safety achievement depends on large volumes of video data and granular anatomical annotations, which are limited by the burden of inefficient manual work. Artificial intelligence (AI) has the potential to radically scale the size of clinical studies by automating operative video analysis.Methods: 481 videos of laparoscopic cholecystectomy were recorded at Christchurch Hospital, New Zealand over three years. AI algorithms analysed the videos, marking time points where the cystic duct and cystic artery were visible and operative phases. Metrics were stratified by surgeon experience (trainee or consultant) and case complexity (North Shore Grading scale). Nine timing metrics were derived based on the outputs of the AI algorithms and compared against the clinical variables.Results: Operative time increased with increasing operative difficulty. Significantly consultants demonstrated a higher proportional duration of anatomy visualisation than trainees in complex patients The cystic duct was commonly identified prior to the cystic artery independent of complexity grade.Conclusion: Surgical video review offers the potential of significant new insights with substantive benefits to patients but is often limited by the costly effort of manual analysis. This paper correlates AI-derived analytics with clinical factors demonstrating real-world utility of AI video analysis.","PeriodicalId":13229,"journal":{"name":"Hpb","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hpb","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.hpb.2025.03.452","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Studys into factors influencing critical view of safety achievement depends on large volumes of video data and granular anatomical annotations, which are limited by the burden of inefficient manual work. Artificial intelligence (AI) has the potential to radically scale the size of clinical studies by automating operative video analysis.

Methods: 481 videos of laparoscopic cholecystectomy were recorded at Christchurch Hospital, New Zealand over three years. AI algorithms analysed the videos, marking time points where the cystic duct and cystic artery were visible and operative phases. Metrics were stratified by surgeon experience (trainee or consultant) and case complexity (North Shore Grading scale). Nine timing metrics were derived based on the outputs of the AI algorithms and compared against the clinical variables.

Results: Operative time increased with increasing operative difficulty. Significantly consultants demonstrated a higher proportional duration of anatomy visualisation than trainees in complex patients The cystic duct was commonly identified prior to the cystic artery independent of complexity grade.

Conclusion: Surgical video review offers the potential of significant new insights with substantive benefits to patients but is often limited by the costly effort of manual analysis. This paper correlates AI-derived analytics with clinical factors demonstrating real-world utility of AI video analysis.

查看原文本刊更多论文

应用人工智能确定影响关键结构识别的临床因素。

背景：对安全成就关键视图影响因素的研究依赖于大量的视频数据和颗粒状的解剖注释，而这些数据受到低效率手工工作负担的限制。人工智能（AI）有可能通过自动化手术视频分析，从根本上扩大临床研究的规模。方法：对新西兰克赖斯特彻奇医院近三年481例腹腔镜胆囊切除术的录像资料进行分析。人工智能算法分析视频，标记胆囊管和囊性动脉可见的时间点和手术阶段。根据外科医生经验（实习医生或咨询医生）和病例复杂性（北岸分级量表）对指标进行分层。根据人工智能算法的输出导出9个时间指标，并与临床变量进行比较。结果：手术难度越大，手术时间越长。值得注意的是，在复杂的患者中，咨询医生比实习生显示出更高比例的解剖显像时间。囊管通常在囊动脉之前被发现，与复杂程度无关。结论：手术视频回顾提供了潜在的重要的新见解，对患者有实质性的好处，但往往受到人工分析的昂贵努力的限制。本文将人工智能衍生的分析与临床因素联系起来，展示了人工智能视频分析在现实世界中的实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Hpb GASTROENTEROLOGY & HEPATOLOGY-SURGERY

CiteScore

5.60

自引率

3.40%

发文量

244

审稿时长

57 days

期刊介绍： HPB is an international forum for clinical, scientific and educational communication. Twelve issues a year bring the reader leading articles, expert reviews, original articles, images, editorials, and reader correspondence encompassing all aspects of benign and malignant hepatobiliary disease and its management. HPB features relevant aspects of clinical and translational research and practice. Specific areas of interest include HPB diseases encountered globally by clinical practitioners in this specialist field of gastrointestinal surgery. The journal addresses the challenges faced in the management of cancer involving the liver, biliary system and pancreas. While surgical oncology represents a large part of HPB practice, submission of manuscripts relating to liver and pancreas transplantation, the treatment of benign conditions such as acute and chronic pancreatitis, and those relating to hepatobiliary infection and inflammation are also welcomed. There will be a focus on developing a multidisciplinary approach to diagnosis and treatment with endoscopic and laparoscopic approaches, radiological interventions and surgical techniques being strongly represented. HPB welcomes submission of manuscripts in all these areas and in scientific focused research that has clear clinical relevance to HPB surgical practice. HPB aims to help its readers - surgeons, physicians, radiologists and basic scientists - to develop their knowledge and practice. HPB will be of interest to specialists involved in the management of hepatobiliary and pancreatic disease however will also inform those working in related fields. Abstracted and Indexed in: MEDLINE® EMBASE PubMed Science Citation Index Expanded Academic Search (EBSCO) HPB is owned by the International Hepato-Pancreato-Biliary Association (IHPBA) and is also the official Journal of the American Hepato-Pancreato-Biliary Association (AHPBA), the Asian-Pacific Hepato Pancreatic Biliary Association (A-PHPBA) and the European-African Hepato-Pancreatic Biliary Association (E-AHPBA).