SynthesisJSLS : Journal of the Society of Laparoendoscopic Surgeons
Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders.
Synthesis in JSLS : Journal of the Society of Laparoendoscopic Surgeons. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review.Neurogastroenterology and motility · 2026Pooled it
- Gut Instincts, Machine Decisions: Evaluating AI Accuracy in the Diagnosis and Treatment of Disorders of Gut-Brain Interaction.Neurogastroenterology and motility · 2026Article
- Artificial intelligence in anal fistula: mapping evidence to IDEAL stages.Annals of coloproctology · 2026Review
- Benign anorectal conditions in a rural referral Sudanese hospital: a four-year retrospective review of patients' characteristics in a low-resource setting.BMC surgery · 2026Article
- Construction and clinical validation of a machine learning-based risk prediction model for poor wound healing after anorectal surgery.Frontiers in surgery · 2026Article
- Multicenter Validation of Video-based Deep Learning to Evaluate Defecation Patterns on 3-dimensional High-definition Anorectal Manometry.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.