Evidence map›Paper›PMID 38910957›Full record

SynthesisJSLS : Journal of the Society of Laparoendoscopic Surgeons

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

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

Abstract readSystematic Review
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. 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 · 2026
    Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maryam AleissaFellow of Colorectal Surgery, Ascension Providence Hospital - Michigan State University, College of Human Medicine, Southfield, Michigan, USA.
Tijani OsumahFellow of Colorectal Surgery, Ascension Providence Hospital - Michigan State University, College of Human Medicine, Southfield, Michigan, USA.
Ernesto DrelichmanAssistant Program Director of Colorectal Surgery Fellowship, Department of Surgery, College of Human Medicine, Ascension Providence Hospital-Michigan State University, Southfield, Michigan, USA.
Vijay MittalAssociate DIO Medical Education, Past Program Director, General Surgery, Department of Surgery, College of Human Medicine, Ascension Providence Hospital-Michigan State University, Southfield, Michigan, USA.
Jasneet BhullarProgram Director of Colorectal Surgery Fellowship, Clinical Assistant Professor WSUCOM/MSUCHM, Department of Surgery, College of Human Medicine, Ascension Providence Hospital-Michigan State University, Southfield, Michigan, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligencePelvic Floor DisordersRectal DiseasesAnus DiseasesFecal IncontinenceHumansManometryAnorectal diseaseArtificial intelligenceDeep learningPelvic floor disorder

Identifiers

PMID38910957
PMCPMC11189024

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.