ReviewCurrent gastroenterology reports2026
Artificial Intelligence in High-resolution Anorectal Manometry: Current Applications and Future Directions.
Review in Current gastroenterology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
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Abstract
purpose of reviewHigh-resolution anorectal manometry (HRAM) has improved anorectal physiological assessment but remains limited by conventional pressure-based metrics that incompletely reflect the complex spatiotemporal physiology of continence and defecation. This review summarizes recent advances in artificial intelligence (AI) and machine learning and their potential to enhance HRAM interpretation and clinical decision-making. RECENT
findingsRecent AI-based approaches have shown promising performance in automated HRAM interpretation, prediction of dyssynergic defecation and balloon expulsion abnormalities, implementation of the London Classification, and analysis of three-dimensional high-definition anorectal manometry datasets. In parallel, integrated physiological metrics such as integrated pressurized volume (IPV) and time-series IPV (TS-IPV) have improved characterization of anorectal function by incorporating pressure amplitude, spatial distribution, and temporal dynamics. Emerging deep-learning models have further demonstrated the potential to identify novel physiological patterns beyond traditional classification systems. AI has the potential to reduce observer variability, improve diagnostic standardization, and facilitate outcome prediction in anorectal physiology testing. Although challenges related to data standardization, external validation, and explainable AI remain, the integration of advanced physiological metrics and AI-driven analytics may transform HRAM from a descriptive diagnostic test into a platform for dynamic physiological phenotyping and precision medicine.
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42826007What OpenQuestion holds
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.