Evaluation of Artificial Intelligence for Adenoma Detection in Water Exchange Colonoscopy: the WEAID Randomized Controlled Trial (Water Exchange With Artificial Intelligence-assisted Detection)
Phase: NA
Conditions: Colon Adenoma, Colonoscopy, Colon Polyp
Sponsor: Evergreen General Hospital, Taiwan

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Official Title: Evaluation of Artificial Intelligence for Adenoma Detection in Water 
Exchange Colonoscopy: the WEAID Randomized Controlled Trial (Water Exchange 
With Artificial Intelligence-assisted Detection) 
NCT number: 06173258 
Date of Document: Oct 1, 2023. 
 
Introduction 
Adenoma detection rate (ADR) and adenoma per colonoscopy (APC) are crucial 
predictors for postcolonoscopy colorectal cancer (CRC). Water Exchange (WE) 
colonoscopy maximizes bowel cleanliness during insertion and has demonstrated 
superiority in increasing ADR and APC as compared to air insufflation. 
Artificial intelligence (AI) lesion computer-aided detection (CADe) devices present 
promising tools for colonoscopies. Meta-Analysis reveals that AI generates an 
average ADR gain of 8% and an average APC gain of 0.20 in air-insufflated 
colonoscopy. 
Recent research confirmed that the addition of AI to WE elevated ADR, and the 
application of WE to AI reduced false positive (FP) instances per colonoscopy, as 
evidenced by the analysis of pre-recorded video in a randomized controlled trial (RCT) 
comparing WE and air insufflation. Pilot retrospective data involving 715 patients 
(367 in the AI group, 348 in the control group) from Italy and Taiwan, with a mean 
age of 59.6 years (51.5% men) showed that WE with AI demonstrated numerical APC 
enhancement (1.31 ± 1.80 vs. 1.14 ± 1.38, P = 0.152) and significantly increased the 
detection rate of proximal sessile serrated lesion (SSL) (5.2% vs. 2.3%, P = 0.044) in 
comparison to WE alone (Tables 1 and 2) (Cheng CL et al. Gastrointest Endosc 
2024;99:AB17–18). 
 

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Table 1. Per-patient ADR and APC in water exchange (WE) with CADe pilot study 
Detection of adenomas 
WE without CADe 
(n=348) 
WE with CADe 
(n=367) 
P value 
Overall ADR, n (%)   
203 (58.3)  
207 (56.4)  
*0.602 
Right ADR, n (%)  
106 (30.5)   
107 (29.2)   
*0.703 
Proximal ADR, n (%)   
132 (37.9)   
149 (40.6)   
*0.465 
Overall APC, mean (SD) 
1.14 (1.38) 
1.31 (1.80) 
†0.152 
*Fisher exact test. †Student's t test 
 
Table 2. Detection of sessile serrated lesions in WE with CADe pilot study 
Detection of SSLs 
WE without 
CADe (n=348) 
WE with 
CADe (n=367) 
P 
value 
SSL detection rate 
Overall SSL detection rate, n (%)  
10/348 (2.9)  
21/367 (5.7)  *0.062 
Right SSL detection rate, n (%)  
7 (2.0) 
14 (3.8) 
*0.153 
Proximal SSL detection rate, n (%)  
8 (2.3) 
19 (5.2) 
*0.044 
Detected SSL per colonoscopy 
Right SSL per colonoscopy, mean (SD) 
0.02 (0.17) 
0.06 (0.36) 
†0.062 
Proximal SSL per colonoscopy, mean (SD) 
0.03 (0.22) 
0.10 (0.58) 
†0.036 
*Fisher exact test. †Student's t test 
 
Hypotheses 
We hypothesize that the combination of WE and AI will significantly increase APC as 
opposed to WE alone, in patients undergoing screening or surveillance colonoscopy. 
The primary outcome of this study is APC. We aim to confirm whether the 
combination of WE with AI achieves a significantly higher APC than WE alone. 
 
Methods 
Study design: The WEAID (WE-AI-assisted Detection) trial is a two-arm parallel 
multicenter RCT designed to compare APC between WE with AI and WE colonoscopy 

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alone. Patient recruitment will be conducted at three hospitals in Italy and Taiwan 
(Digestive Endoscopy Unit, CTO Hospital, Iglesias, Italy [primary investigator: Sergio 
Cadoni]; Digestive Endoscopy and Gastroenterology Unit, Manzoni Hospital, Lecco, 
Italy [primary investigator: Arnaldo Amato]; and Evergreen General Hospital, Taoyuan, 
Taiwan [primary investigator: Chi-Liang Cheng]). Ethical approval will be sought from 
the Institutional Review Boards of the participating centers. Informed written 
consent will be obtained from all enrolled patients. The trial has been registered with 
ClinicalTrials.gov and assigned the identifier NCT06173258 before patient enrollment. 
The Sepulveda Ambulatory Care Center, VAGLAHS, UCLA in the USA will be a 
non-recruiting participating site. 
 
Study population: The study population include male and female patients aged 
45─75 years at average risk for CRC who plan to undergo colonoscopy for primary 
screening, postpolypectomy surveillance, and individuals with positive fecal 
immunochemical test (FIT) or guaiac fecal occult blood test (gFOBT) results. The 
exclusion criteria incorporate (1) patients with a history of inflammatory bowel 
disease, hereditary CRC syndrome, serrated polyposis syndrome, CRC, colorectal 
resection, colonic stricture, or severe comorbid illnesses rendering polypectomy 
unsafe; (2) patients with colonoscopy contraindications (e.g., acute diverticulitis or 
toxic megacolon); (3) therapeutic colonoscopy (e.g., hemostasis, removal of a large 
polyp); (4) emergent colonoscopy; (5) pregnant women or those planning pregnancy; 
and (6) patients unwilling to participate in the study. 
 
Study investigators: All study investigators will be experts with a minimum of 500 
completed WE colonoscopies. Additionally, each investigator will exhibit a minimum 
baseline ADR of 25%. 

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Randomization: Enrolled patients will be randomized in a 1:1 ratio to either the WE 
with AI-assisted colonoscopy (study group) or WE colonoscopy alone (control group). 
Randomization will be carried out by computer-generated sequences using a block 
design (four participants per block). Stratification will be based on study sites, 
colonoscopists, and colonoscopy indications (primary screening, surveillance, and 
positive FIT or gFOBT).  
 
Artificial intelligence: Commercially available AI system (CAD-EYE, Fujifilm, EU and 
Taiwan; Endo-AID, Olympus, EU and Taiwan) will be employed. During AI-assisted 
procedures, the AI will be activated during the withdrawal phase of the procedure, 
providing a bounding box as output any time a lesion that is suspected to be a polyp 
is recognized by the CADe device. At each center, expertise in AI was already 
developed prior to the start of the study. 
 
Data collection: To capture all study data comprehensively, a case record form will be 
employed. This form, available in written format, will document various aspects of 
the study, including enrollment procedures, randomization, colonoscopy details, 
histological results, and safety information. Study investigators and their teams are 
responsible for completing all sections of a pre-built Microsoft Excel worksheet.  
 
Colonoscopy procedure: Bowel preparation regimens will align with established 
standard practices at each individual site. The study procedures employ standard 
high-definition colonoscopy video processors with integrated AI systems. In both 
arms of the study, insertion and withdrawal of the colonoscope will be executed by 
the same colonoscopist. The evaluation and grading of bowel preparation quality will 

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be carried out using the Boston Bowel Preparation Scale scoring system. 
During the insertion phase of WE colonoscopy, the air pump will be turned off, 
while the colon will be irrigated with water using a flushing pump. The WE approach 
involves the simultaneous infusion of water to facilitate luminal expansion and 
suction of unclean water during insertion. Any encountered air pockets will be 
aspirated to ensure optimal WE maneuver in salvage cleaning. Upon reaching the 
cecum, where most of the water is suctioned to collapse the cecal lumen, the air 
pump will be opened. All investigators will be asked to adhere to WE technique 
confirmation of nearly equivalent volumes of water infused and suctioned upon cecal 
arrival. During the withdrawal phase, air or CO2 will be insufflated in both arms. 
Withdrawal from the cecum will begin in the left lateral position. Similar 
withdrawal techniques with adequate luminal distention and comprehensive 
examination will be emphasized. Adhering to prevailing guidelines set by the 
American and European Societies of Gastrointestinal Endoscopy, a minimum of 6 
minutes of clean withdrawal time will be maintained for all colonoscopies across 
both arms. 
During the withdrawal phase, an exhaustive polyp search and subsequent 
resection will be performed in both study arms. Comprehensive records will be 
maintained for all detected polyps, capturing size (assessed in comparison with open 
forceps or snare), precise location, and morphology (classified according to the Paris 
classification). 
 
False positives of AI: False positive (FP) activation is defined as the identification by 
AI systems of an area during the withdrawal phase that is not deemed to be a 
colorectal lesion after re-examination by the colonoscopist. Real-time flagging of FP 
activation is recorded by investigators and study staff. The NOISE classification will be 

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used to categorize the causes of FP activations. 
 
Study outcomes: The primary outcome is APC, calculated as the total number of 
adenomas detected divided by the total number of colonoscopies performed. 
Secondary outcomes encompass 1) overall ADR, 2) ADR and APC in the right and 
proximal colon, 3) SSL detection rates, 4) mean number of non-neoplastic lesions 
resected per colonoscopy, 5) real-time number of FPs per colonoscopy, and 6) 
procedural times (e.g., insertion time, withdrawal time, and total procedural time). 
 
Sample size calculation: For group comparison, the sample size was determined 
assuming that the APC of the control group is 0.9. A clinically significant relative 
increase of 25% in APC was considered. With a two-sided significance level of 5% 
and 80% power, 376 patients will be required per group (752 randomized 
patients). Accounting for a 10% overall dropout rate, enrollment of at least 836 
patients is planned. 
 
Statistical analysis: Categorical variables are presented as frequencies and 
percentages and continuous variables as means with standard deviations. The 
analysis will follow the intention-to-treat approach. Student's t-test will be used for 
continuous variables and chi-square test will be used for categorical variables. 
Poisson regression will be used to calculate incident rate ratios comparing the APC 
between the two arms, adjusting for age, sex, body mass index, smoking history, 
CADe system, and bowel preparation quality. A planned interim analysis will be 
conducted after the recruitment of approximately 70% of the planned enrollment to 
determine whether early termination of the study is appropriate to minimize the 
number of patients in the control group given the less effective, or clearly inferior 

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treatment. All statistical analyses will be performed using Python version 3.10 (SciPy 
Library). A P value < 0.05 is considered significant.