Evidence map›Paper›PMID 42447478›Full record

Trial reportJMIR mHealth and uHealth2026

Use of an AI Image Recognition-Based Smartphone App in Personalized Bowel Preparation Before Colonoscopy: Prospective Randomized Controlled Trial.

Fei Han, Shilin Gao, Hanqiang Zhan, Nan Wang, Zhaoxiang Song, Jinlin Xie, Yuhan Hou, Yimeng Deng, Jianning Yao

Registry-linked trialAbstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06610630 (Research on the Application of AI Image Recognition-Based Smartphone Apps in Personalized Bowel Preparation), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

NCT06610630 nacompletednot on this map

Research on the Application of AI Image Recognition-Based Smartphone Apps in Personalized Bowel Preparation

TypeinterventionalSponsorJianning YaoRan2024 to 2025Enrolled513ConditionsColorectal PolypsArmsAssessment of Bowel Preparation Using AI-Driven Smartphone Applications
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Fei Han *The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0008-3494-4276
Shilin Gao *The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0008-5485-776X
Hanqiang ZhanThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0008-9099-1202
Nan WangThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0008-2557-8761
Zhaoxiang SongThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0007-0680-0438
Jinlin XieThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0004-8944-6937
Yuhan HouThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0007-2235-591X
Yimeng DengThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0003-2132-1065
Jianning YaoThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.ORCID 0009-0002-9280-1931

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSufficient bowel preparation is critical for increasing the quality of colonoscopy. However, current bowel preparation guidelines have limitations. We used a constructed and validated convolutional neural network model to assist patients in assessing the adequacy of bowel preparation and guide the use of laxatives based on individual variations.

objectiveThis research intended to explore the influence of AI-guided bowel preparation on patients' use of laxatives and bowel cleansing quality.

methodsWe conducted a prospective randomized controlled trial that included patients who had a colonoscopy at the First Affiliated Hospital of Zhengzhou University and used sodium phosphate solution for bowel cleansing. Included individuals were randomly categorized into 2 groups: the AI-guided group, which used an AI program to assess the adequacy of bowel cleansing and stopped the use of laxatives once the assessment was passed, and the control group, which evaluated bowel preparation adequacy based on standard practices and used the recommended doses of laxatives. The primary outcomes were the amount of laxatives used and the rate of adequate bowel cleansing based on the Boston Bowel Preparation Scale (BBPS). Other outcomes included overall BBPS scores, BBPS scores by colon region, adenoma detection rate (ADR), polyp detection rate, and advanced ADR, as well as adverse reactions and compliance.

resultsThis study included 513 patients (AI-guided group: n=255, 49.7%; control group: n=258, 50.3%). The volume of bowel-cleansing solution consumed by patients in the AI-guided group was 1245.5 (SD 377.2) mL, significantly lower than the 1586.2 (SD 50.4) mL in the control group (P<.001). Reduced use of laxatives was independently predicted by younger age, lower BMI, and higher defecation frequency. The rate of adequate bowel preparation in the AI group was not inferior to that in the control group (238/255, 93.3% vs 229/258, 88.8%, respectively; 1-sided P<.001 for noninferiority). No significant differences were observed in segmental or total BBPS scores, ADR, polyp detection rate, and advanced ADR between the 2 groups. Additionally, the medication adherence rate in the AI-guided group was significantly higher than in the control group (247/255, 96.9% vs 236/258, 91.5%, respectively; P=.009), whereas the incidence of adverse reactions was notably lower (5/255, 2% vs 15/258, 5.8%, respectively; P=.02).

conclusionsUsing the AI-driven bowel preparation program to assist patients in personalized bowel preparation can reduce unnecessary use of laxatives while achieving results comparable to those of standard practices.

trial registrationClinicalTrials.gov NCT06610630; https://clinicaltrials.gov/study/NCT06610630.

Indexed as

Artificial IntelligenceColonoscopyMobile ApplicationsAdultAgedCatharticsChinaFemaleHumansLaxativesMaleMiddle AgedPreoperative CareProspective StudiesSmartphoneCatharticsLaxativesAIartificial intelligencebowel preparationcolonoscopysmartphone

Identifiers

PMID42447478
PMCPMC13416303

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.