Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders.

Maryam Aleissa, Tijani Osumah, Ernesto Drelichman, Vijay Mittal, Jasneet Bhullar
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Abstract

Background: Anorectal diseases and pelvic floor disorders are prevalent among the general population. Patients may present with overlapping symptoms, delaying diagnosis, and lowering quality of life. Treating physicians encounter numerous challenges attributed to the complex nature of pelvic anatomy, limitations of diagnostic techniques, and lack of available resources. This article is an overview of the current state of artificial intelligence (AI) in tackling the difficulties of managing benign anorectal disorders and pelvic floor disorders.

Methods: A systematic literature review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched the PubMed database to identify all potentially relevant studies published from January 2000 to August 2023. Search queries were built using the following terms: AI, machine learning, deep learning, benign anorectal disease, pelvic floor disorder, fecal incontinence, obstructive defecation, anal fistula, rectal prolapse, and anorectal manometry. Malignant anorectal articles and abstracts were excluded. Data from selected articles were analyzed.

Results: 139 articles were found, 15 of which met our inclusion and exclusion criteria. The most common AI module was convolutional neural network. researchers were able to develop AI modules to optimize imaging studies for pelvis, fistula, and abscess anatomy, facilitated anorectal manometry interpretation, and improved high-definition anoscope use. None of the modules were validated in an external cohort.

Conclusion: There is potential for AI to enhance the management of pelvic floor and benign anorectal diseases. Ongoing research necessitates the use of multidisciplinary approaches and collaboration between physicians and AI programmers to tackle pressing challenges.

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人工智能在肛门直肠疾病和盆底障碍中的现状和作用。
背景:肛门直肠疾病和盆底障碍在普通人群中很普遍。患者可能会出现重叠症状,延误诊断,降低生活质量。由于盆腔解剖的复杂性、诊断技术的局限性以及可用资源的缺乏,治疗医生遇到了许多挑战。本文概述了人工智能(AI)在解决良性肛门直肠疾病和盆底疾病管理难题方面的现状:方法:根据《系统综述和元分析首选报告项目》指南进行了系统性文献综述。我们检索了 PubMed 数据库,以确定 2000 年 1 月至 2023 年 8 月期间发表的所有潜在相关研究。搜索查询使用了以下术语:人工智能、机器学习、深度学习、良性肛门直肠疾病、盆底障碍、大便失禁、排便障碍、肛瘘、直肠脱垂和肛门直肠测压。恶性肛门直肠病文章和摘要被排除在外。对所选文章的数据进行了分析:结果:共找到 139 篇文章,其中 15 篇符合我们的纳入和排除标准。最常见的人工智能模块是卷积神经网络。研究人员能够开发人工智能模块来优化骨盆、瘘管和脓肿解剖的成像研究,促进肛门直肠测压的解释,并改善高清肛门镜的使用。这些模块均未在外部队列中得到验证:结论:人工智能有可能加强盆底和良性肛门直肠疾病的治疗。正在进行的研究需要使用多学科方法以及医生和人工智能程序员之间的合作,以应对紧迫的挑战。
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来源期刊
CiteScore
2.40
自引率
0.00%
发文量
69
审稿时长
4-8 weeks
期刊介绍: JSLS, Journal of the Society of Laparoscopic & Robotic Surgeons publishes original scientific articles on basic science and technical topics in all the fields involved with laparoscopic, robotic, and minimally invasive surgery. CRSLS, MIS Case Reports from SLS is dedicated to the publication of Case Reports in the field of minimally invasive surgery. The journals seek to advance our understandings and practice of minimally invasive, image-guided surgery by providing a forum for all relevant disciplines and by promoting the exchange of information and ideas across specialties.
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