Exploring the Clinical Value of an AI-Assisted Patient Self-Assessment App for Bowel Preparation: A Multicenter Study
Phase: NA
Conditions: Bowel Preparation for Colonoscopy, Colonoscopy
Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University

Study Protocol
Protocol ID：2025ZSLYEC-747
Title: Exploring the Clinical Value of an AI-Assisted Patient Self-Assessment App for Bowel
Preparation: A Multicenter Study
Document date: 9,1, 2025

Background and aim
Colonoscopy is a key tool for screening, diagnosing and treating colorectal cancer. Early
detection and removal of polyps can substantially reduce both the clinical burden and the
economic cost of advanced-stage disease. Adenoma detection rate (ADR)—the proportion of
colonoscopies in which at least one adenoma is found—is the principal quality indicator; every
1 % drop in ADR increases subsequent cancer risk by 3 %. Adequate bowel preparation improves
ADR, cuts the miss rate for polyps and cancers, and lowers post-colonoscopy cancer incidence,
whereas poor preparation decreases polyp/ tumour detection, prolongs insertion time, increases
endoscopist fatigue and may raise procedure-related complications. Yet 20–25 % of examinations
are still performed under sub-optimal conditions.
The quality of cleansing depends largely on patient compliance with the purgative regimen,
so intensive education is essential. Telephone calls, text messages, short videos and mini-programs
have all been shown to improve compliance and preparation quality. However, early identification
of individuals who are likely to prep poorly—and the opportunity to rescue them—remains an
unmet need. The character of the last stool after laxative intake correlates closely with overall
preparation quality; staff often ask patients to describe this stool to decide whether to proceed or
delay the examination, but this is reactive rather than proactive. Allowing patients to self-assess
and, if necessary, present early (out-patients) or alert staff (in-patients) for rescue could convert
sub-optimal into adequate preparation.
Artificial intelligence is now widely used in gastrointestinal endoscopy. In our pilot work we
developed a smartphone app that uses AI to grade stool photographs. In an internal validation set
the algorithm achieved 100 % sensitivity (95 % CI 34.2–100 %) and 94.1 % specificity (90.1–
96.5 %) for binary classification of adequacy. We then conducted a single-centre RCT; 461
patients (229 control, 232 app) were analysed. App users had significantly higher
adequate-preparation rates (97 % vs 90 %, P = 0.002) and excellent-preparation rates (86.2 % vs
59.8 %, P < 0.001), and higher mean Boston Bowel Preparation Scale (BBPS) scores (8.2 ± 0.8
vs 7.8 ± 1.2, P < 0.001) as well as segment-specific BBPS sub-scores. Cecal intubation rate,
polyp-detection rate (PDR) and ADR did not differ between groups. These findings suggest the
app improves preparation quality, but the single-centre design limits generalisability. We now
propose a multicentre study to confirm the clinical value of this AI-assisted self-assessment tool.

Methods
Study design: a multicenter, randomized controlled clinical trial.
Location: The Sixth Affiliated Hospital, Sun Yat-sen UniversitTianjin Haihe Hospital
Guangdong Second Provincial General Hospital
Dongguan Humen Hospital of Traditional Chinese Medicine
Cangzhou Central Hospital
The inclusion criteria:
(1) Age ≥18 years
(2) Undergoing colonoscopy at a participating site
(3) Able to defecate in a toilet (or in a setting where stool characteristics can be observed) after
taking the bowel-prep solution
(4) Proficient in using a smartphone
(5) Willing to participate voluntarily
The exclusion criteria:
①American Society of Anesthesiologists (ASA) class III or IV;
②Gastric-outlet or intestinal obstruction;
③Active gastrointestinal bleeding;
④Enterostomy (colostomy/ileostomy);
⑤Status post total colectomy.
Intervention
After taking the bowel-prep solution, patients in the app group are instructed to photograph their
final stool in the toilet and upload the image through our smartphone app. The AI algorithm
immediately scores the prep quality. If the image is rated “poor” or “inadequate,” the app displays
an alert advising the patient to come to the hospital early or contact clinical staff; clinical staffs
then review the photo and decide whether a rescue preparation is needed. The standard rescue was
an additional packet of polyethylene glycol. If the image is rated “adequate,” the patient is told the
prep is acceptable and should proceed to the appointment, where clinical staffs will use the
uploaded photo to confirm eligibility for colonoscopy.
The control group followed the conventional procedure: before the examination, clinical staffs
asked questions and relied on the patient’s verbal description of their stool to judge the adequacy

of bowel preparation. If the prep was deemed inadequate, the standard rescue was an additional
packet of polyethylene glycol.
Outcome parameters
The primary outcome parameter is the comparison of the bowel preparation quality between the
app group and the control group ( the rate of adequate and excellent bowel preparation).
The secondary outcome parameters are: cecal intubation rate
cecal intubation time
withdrawal time (excluding polypectomy and biopsy)
polyp-detection rate (PDR)
adenoma-detection rate (ADR)
advanced-adenoma-detection rate (aADR)
use of rescue preparation
rate of rescheduled examinations due to inadequate preparation
clinical staffs’ assessment time
user satisfaction score,
and post-procedural adverse events.
The results will be analyzed in full analysis set, per-protocol set and safety set.
Sample Size
Assuming that 16 % of patients have inadequate bowel preparation with the traditional regimen,
and the app is expected to convert 25 % of these failures (i.e., 4 % of the total cohort) to adequate
preparation, a sample size of 235 participants is required for α = 0.05 and power = 0.80.
Allowing for a 10 % dropout rate, we plan to enroll 262 subjects per arm, for a total of 524
participants.
Randomization
Stratified randomization by center. Random numbers generated in Excel by an independent
statistician; sequentially numbered, sealed, opaque envelopes prepared and held by the third party.
The envelopes constitute the allocation sequence; random numbers remain concealed from
patients and endoscopists until the envelope is opened immediately before allocation.
Blinding
Patients cannot be blinded; operators, outcomes assessors, and pathologists will remain blinded.

Statistical analysis
All analyses were performed with SPSS version 26. Baseline characteristics were examined to
assess balance across concentration groups. Normally distributed continuous variables are
presented as mean ± SD and compared between groups using independent-samples t-tests;
non-normally distributed continuous variables are expressed as median (interquartile range) and
compared with the Wilcoxon rank-sum test. Categorical rates were compared using the χ² test. A
two-sided P < 0.05 was considered statistically significant.
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