AI-Based Stool Image Analysis for Colorectal Neoplasia Risk Assessment

NCT07740122 | Recruiting

• 1 other identifier: HSR-FAEX-CRC-FIT-2026
• Study Type: observational
• Target: 250
• Locations: 1 country
• Sites: 1

Brief Summary

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

Colorectal Neoplasms

Keywords

Colorectal Cancer Screening; Artificial Intelligence; Stool Image Analysis; Fecal Immunochemical Test; Colonoscopy; Digital Health

Outcome Measures

Primary Outcomes (1)

• Correlation Between AI-Derived Stool Image Score and Quantitative FIT (faecal immunochemical test) Concentration

  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.

  Within 90 days of stool image submission

Secondary Outcomes (3)

• Area Under the ROC Curve for FIT Positivity

  Within 90 days of stool image submission

• Sensitivity and Specificity of the AI-Derived Stool Image Score for FIT Positivity/Negativity

  Within 90 days of stool image submission

• Area Under the ROC Curve for Colonoscopy-Detected Colorectal Neoplasia

  Within 90 days of stool image submission

Study Arms (1)

Prospective Stool Image, FIT and Colonoscopy Cohort

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.

Diagnostic Test: AI-Based Stool Image Analysis

Interventions

AI-Based Stool Image Analysis DIAGNOSTIC_TEST

Participants capture stool images using the FAEX Health mobile application. Coded images are analyzed using artificial intelligence algorithms to derive visual features and a prespecified patient-level output or score. The AI-derived output is used exclusively for research and is compared primarily with quantitative FIT results and FIT positivity, with secondary comparisons against colonoscopy and histopathology when available. The output is not used to provide a diagnosis or guide clinical management.

Prospective Stool Image, FIT and Colonoscopy Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adults receiving care at Hospital Dr. Sótero del Río who are undergoing colorectal cancer screening or diagnostic evaluation and have a quantitative fecal immunochemical test planned or completed. Eligible participants will prospectively submit stool images through the FAEX Health mobile application. AI-derived image features will be compared primarily with matched quantitative FIT results and FIT positivity. Colonoscopy and histopathology findings will be evaluated as secondary exploratory outcomes when available. AI results will not be returned to participants or clinicians and will not influence clinical management.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Servicio de Salud Metropolitano Sur Oriente: lead

Study Sites (1)

Hospital Sotero del Rio

Santiago, Santiago Metropolitan, 8207257, Chile

RECRUITING

Related Publications (9)

• Lee 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 BACKGROUND

• Collins 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 BACKGROUND

• Sounderajah 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 BACKGROUND

• Zhong 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 BACKGROUND

• Ramprasad 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.

  PMID: 39866713 BACKGROUND

• Katsoula A, Paschos P, Haidich AB, Tsapas A, Giouleme O. Diagnostic Accuracy of Fecal Immunochemical Test in Patients at Increased Risk for Colorectal Cancer: A Meta-analysis. JAMA Intern Med. 2017 Aug 1;177(8):1110-1118. doi: 10.1001/jamainternmed.2017.2309.

  PMID: 28628706 BACKGROUND

• Rahman F, Trivedy M, Rao C, Akinlade F, Mansuri A, Aggarwal A, Laskaratos FM, Rajendran N, Banerjee S. Faecal Immunochemical Testing to Detect Colorectal Cancer in Symptomatic Patients: A Diagnostic Accuracy Study. Diagnostics (Basel). 2023 Jul 10;13(14):2332. doi: 10.3390/diagnostics13142332.

  PMID: 37510076 BACKGROUND

• Bailey JA, Weller J, Chapman CJ, Ford A, Hardy K, Oliver S, Morling JR, Simpson JA, Humes DJ, Banerjea A. Faecal immunochemical testing and blood tests for prioritization of urgent colorectal cancer referrals in symptomatic patients: a 2-year evaluation. BJS Open. 2021 Mar 5;5(2):zraa056. doi: 10.1093/bjsopen/zraa056.

  PMID: 33693553 BACKGROUND

• D'Souza N, Georgiou Delisle T, Chen M, Benton S, Abulafi M; NICE FIT Steering Group. Faecal immunochemical test is superior to symptoms in predicting pathology in patients with suspected colorectal cancer symptoms referred on a 2WW pathway: a diagnostic accuracy study. Gut. 2021 Jun;70(6):1130-1138. doi: 10.1136/gutjnl-2020-321956. Epub 2020 Oct 21.

  PMID: 33087488 BACKGROUND

MeSH Terms

Conditions

Colorectal Neoplasms

Condition Hierarchy (Ancestors)

Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases

Study Officials

• Erik Manriquez Alegria, MD

  Hospital Sotero Del Rio

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Erik Manriquez Alegria, MD

CONTACT

+56988232111; erik.manriquez.alegria@gmail.com

Felipe Quezada Diaz, MD

CONTACT

+56934651992; ffquezad@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 28, 2026
• First Posted: July 31, 2026
• Study Start: May 8, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: July 31, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

CL (1)