Clinical trial · Observational
AI-Based Stool Image Analysis for Colorectal Neoplasia Risk Assessment
FECAL-AI: Prospective Observational Validation of AI-Based Stool Image Analysis Against Quantitative Fecal Immunochemical Testing for Colorectal Neoplasia Risk Assessment
NCT07740122CI-TRIAL-00120143FECAL-AIrecruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This prospective observational substudy evaluates the association between artificial intelligence-derived features from stool images analyzed using the FAEX Health digital platform and fecal immunochemical test results in adults undergoing colorectal cancer screening or diagnostic evaluation. Participants will capture stool images using a mobile application. The primary analysis will compare AI-derived image outputs with quantitative FIT values and FIT positivity. Secondary exploratory analyses will assess associations with colonoscopy and histopathological findings when these results are available. The platform will be used exclusively for research and will not provide diagnoses, replace clinical evaluation, or influence medical decisions.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colorectal Neoplasms | Colorectal Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-Based Stool Image Analysis | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Prospective Stool Image, FIT and Colonoscopy Cohort
- description
- Adults participating in colorectal cancer screening or diagnostic evaluation who submit stool images through the FAEX Health mobile application. AI-derived stool image outputs will be compared primarily with quantitative fecal immunochemical test results and FIT positivity. Colonoscopy and histopathology findings will be evaluated as secondary exploratory outcomes when available. AI-derived results will not be returned to participants or clinicians and will not influence clinical decisions.
- interventionNames
- Diagnostic Test: AI-Based Stool Image Analysis
Primary outcomes (1)
- measure
- Correlation Between AI-Derived Stool Image Score and Quantitative FIT (faecal immunochemical test) Concentration
- timeFrame
- Within 90 days of stool image submission
- description
- Correlation coefficient between the prespecified patient-level AI-derived stool image score and quantitative fecal immunochemical test concentration among participants with analyzable matched data, reported with a 95% confidence interval.
Secondary outcomes (3)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Age 18 years or older. * Referred for screening or diagnostic colonoscopy at Hospital Dr. Sótero del Río. * Quantitative fecal immunochemical testing planned or completed within 30 days before or after stool image submission. * Able to submit at least one stool image using the FAEX Health mobile application, independently or with assistance. * Able and willing to provide written informed consent. Exclusion Criteria: * Unable or unwilling to provide written informed consent. * Previous enrollment in the study. * Unable to complete stool-image capture, even with assistance. * Study images and clinical data cannot be reliably linked using the assigned study code.
References
Publications (9)
- BACKGROUNDLee JW, Woo D, Kim KO, Kim ES, Kim SK, Lee HS, Kang B, Lee YJ, Kim J, Jang BI, Kim EY, Jo HH, Chung YJ, Ryu H, Park SK, Park DI, Yu H, Jeong S; IBD Research Group of KASID and Crohn's and Colitis Association in Daegu-Gyeongbuk (CCAiD). Deep Learning Model Using Stool Pictures for Predicting Endoscopic Mucosal Inflammation in Patients With Ulcerative Colitis. Am J Gastroenterol. 2025 Jan 1;120(1):213-224. doi: 10.14309/ajg.0000000000002978. Epub 2024 Jul 25. PMID 39051648
- BACKGROUNDCollins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378. PMID 38626948
- BACKGROUNDSounderajah V, Guni A, Liu X, Collins GS, Karthikesalingam A, Markar SR, Golub RM, Denniston AK, Shetty S, Moher D, Bossuyt PM, Darzi A, Ashrafian H; STARD-AI Steering Committee. The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence. Nat Med. 2025 Oct;31(10):3283-3289. doi: 10.1038/s41591-025-03953-8. Epub 2025 Sep 15. PMID 40954311
- BACKGROUNDZhong H, Hou C, Huang Z, Chen X, Zou Y, Zhang H, Wang T, Wang L, Huang X, Xiang Y, Zhong M, Hu M, Xiong D, Wang L, Zhang Y, Luo Y, Guan Y, Xia M, Liu X, Yang J, Gan T, Wei W, Chen H, Gong H. A clinical pilot trial of an artificial intelligence-driven smart phone application of bowel preparation for colonoscopy: a randomized clinical trial. Scand J Gastroenterol. 2025 Jan;60(1):116-121. doi: 10.1080/00365521.2024.2443520. Epub 2024 Dec 22. PMID 39709551
- BACKGROUNDRamprasad C, Saini D, Del Carmen H, Krasnovsky L, Chandra R, Mcgregor R, Shinohara RT, Eaton E, Gummadi M, Mehta S, Lewis JD. Text Message System for the Prediction of Colonoscopy Bowel Preparation Adequacy Before Colonoscopy: An Artificial Intelligence Image Classification Algorithm Based on Images of Stool Output. Gastro Hep Adv. 2024 Sep 19;4(2):100556. doi: 10.1016/j.gastha.2024.09.011. eCollection 2025.