Systematic review on the use of artificial intelligence to identify anatomical structures during laparoscopic cholecystectomy: a tool towards the future.

IF 2.1 3区医学 Q2 SURGERY

Langenbeck's Archives of Surgery Pub Date : 2025-03-18 DOI:10.1007/s00423-025-03651-6

Diletta Corallino, Andrea Balla, Diego Coletta, Daniela Pacella, Mauro Podda, Annamaria Pronio, Monica Ortenzi, Francesca Ratti, Salvador Morales-Conde, Pierpaolo Sileri, Luca Aldrighetti

{"title":"Systematic review on the use of artificial intelligence to identify anatomical structures during laparoscopic cholecystectomy: a tool towards the future.","authors":"Diletta Corallino, Andrea Balla, Diego Coletta, Daniela Pacella, Mauro Podda, Annamaria Pronio, Monica Ortenzi, Francesca Ratti, Salvador Morales-Conde, Pierpaolo Sileri, Luca Aldrighetti","doi":"10.1007/s00423-025-03651-6","DOIUrl":null,"url":null,"abstract":"Purpose: Bile duct injury (BDI) during laparoscopic cholecystectomy (LC) is a dreaded complication. Artificial intelligence (AI) has recently been introduced in surgery. This systematic review aims to investigate whether AI can guide surgeons in identifying anatomical structures to facilitate safer dissection during LC.Methods: Following PROSPERO registration CRD-42023478754, a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of MEDLINE (via PubMed), EMBASE, and Web of Science databases was conducted.Results: Out of 2304 articles identified, twenty-five were included in the analysis. The mean average precision for biliary structures detection reported in the included studies reaches 98%. The mean intersection over union ranges from 0.5 to 0.7, and the mean Dice/F1 spatial correlation index was greater than 0.7/1. AI system provided a change in the annotations in 27% of the cases, and 70% of these shifts were considered safer changes. The contribution to preventing BDI was reported at 3.65/4.Conclusions: Although studies on the use of AI during LC are few and very heterogeneous, AI has the potential to identify anatomical structures, thereby guiding surgeons towards safer LC procedures.","PeriodicalId":17983,"journal":{"name":"Langenbeck's Archives of Surgery","volume":"410 1","pages":"101"},"PeriodicalIF":2.1000,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11919950/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Langenbeck's Archives of Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00423-025-03651-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose: Bile duct injury (BDI) during laparoscopic cholecystectomy (LC) is a dreaded complication. Artificial intelligence (AI) has recently been introduced in surgery. This systematic review aims to investigate whether AI can guide surgeons in identifying anatomical structures to facilitate safer dissection during LC.

Methods: Following PROSPERO registration CRD-42023478754, a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of MEDLINE (via PubMed), EMBASE, and Web of Science databases was conducted.

Results: Out of 2304 articles identified, twenty-five were included in the analysis. The mean average precision for biliary structures detection reported in the included studies reaches 98%. The mean intersection over union ranges from 0.5 to 0.7, and the mean Dice/F1 spatial correlation index was greater than 0.7/1. AI system provided a change in the annotations in 27% of the cases, and 70% of these shifts were considered safer changes. The contribution to preventing BDI was reported at 3.65/4.

Conclusions: Although studies on the use of AI during LC are few and very heterogeneous, AI has the potential to identify anatomical structures, thereby guiding surgeons towards safer LC procedures.

查看原文本刊更多论文

在腹腔镜胆囊切除术中使用人工智能识别解剖结构的系统综述：一种面向未来的工具。

目的：腹腔镜胆囊切除术（LC）中胆管损伤（BDI）是一种可怕的并发症。人工智能（AI）最近被引入到外科手术中。本系统综述旨在探讨人工智能是否可以指导外科医生识别解剖结构，以促进LC中更安全的解剖。方法：按照PROSPERO注册编号CRD-42023478754，对MEDLINE（通过PubMed）、EMBASE和Web of Science数据库进行符合PRISMA标准的系统评价和meta分析首选报告项目（Preferred Reporting Items for Systematic Reviews and meta - analysis）系统搜索。结果：在鉴定的2304篇文章中，有25篇被纳入分析。在纳入的研究中，胆道结构检测的平均精度达到98%。交汇的均值在0.5 ~ 0.7之间，Dice/F1空间相关指数均值大于0.7/1。人工智能系统在27%的情况下对注释进行了更改，其中70%的更改被认为是更安全的更改。预防BDI的贡献为3.65/4。结论：尽管关于人工智能在LC中的应用的研究很少，而且非常多样化，但人工智能具有识别解剖结构的潜力，从而指导外科医生采取更安全的LC手术。

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

Langenbeck's Archives of Surgery 医学-外科

CiteScore

3.30

自引率

8.70%

发文量

342

审稿时长

4-8 weeks

期刊介绍： Langenbeck''s Archives of Surgery aims to publish the best results in the field of clinical surgery and basic surgical research. The main focus is on providing the highest level of clinical research and clinically relevant basic research. The journal, published exclusively in English, will provide an international discussion forum for the controlled results of clinical surgery. The majority of published contributions will be original articles reporting on clinical data from general and visceral surgery, while endocrine surgery will also be covered. Papers on basic surgical principles from the fields of traumatology, vascular and thoracic surgery are also welcome. Evidence-based medicine is an important criterion for the acceptance of papers.