AI-Based Stool Image Analysis for Colorectal Neoplasia Risk Assessment (FECAL-AI)
28. juli 2026 oppdatert av: Servicio de Salud Metropolitano Sur Oriente
FECAL-AI: Prospective Observational Validation of AI-Based Stool Image Analysis Against Quantitative Fecal Immunochemical Testing for Colorectal Neoplasia Risk Assessment
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.
Studieoversikt
Status
Rekruttering
Forhold
Intervensjon / Behandling
Detaljert beskrivelse
This is a substudy of the project "Estrategia de prevención secundaria de cáncer colorrectal en personas mayores a 18 años." The substudy evaluates the FAEX Health digital platform, which applies artificial intelligence algorithms to stool images for non-diagnostic research and validation.
Participants will capture images of their stools using the FAEX Health mobile application. The images will be coded and analyzed using computational algorithms designed to identify visual characteristics such as color, consistency, and possible visible blood.
The primary objective is to evaluate the association and discriminatory performance of AI-derived stool image features for quantitative fecal immunochemical test results and FIT positivity. Secondary exploratory objectives are to assess associations between these image features and colonoscopic and histopathological findings among participants for whom these results are available.
The AI-derived results will not be returned to participants or treating clinicians and will not be used to determine whether colonoscopy or any other clinical procedure is performed. The findings may inform future studies evaluating stool image analysis as a potential triage strategy when FIT is unavailable or declined; however, the present study does not evaluate the platform as a replacement for FIT.
Personal identifiers will not be stored together with stool images. Access to coded study information will be restricted to authorized researchers, and study data will be managed according to applicable ethical, legal, and confidentiality requirements.
Studietype
Observasjonsmessig
Registrering (Antatt)
250
Kontakter og plasseringer
Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.
Studiekontakt
- Navn: Erik Manriquez Alegria, MD
- Telefonnummer: +56988232111
- E-post: erik.manriquez.alegria@gmail.com
Studer Kontakt Backup
- Navn: Felipe Quezada Diaz, MD
- Telefonnummer: +56934651992
- E-post: ffquezad@gmail.com
Studiesteder
-
-
Santiago Metropolitan
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Santiago, Santiago Metropolitan, Chile, 8207257
- Rekruttering
- Hospital Sotero Del Rio
-
Ta kontakt med:
- Erik Manriquez Alegria, MD
- Telefonnummer: +56988232111
- E-post: erik.manriquez.alegria@gmail.com
-
Ta kontakt med:
- Felipe Quezada Diaz, MD
- Telefonnummer: +56934651992
- E-post: ffquezad@gmail.com
-
-
Deltakelseskriterier
Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
- Voksen
- Eldre voksen
Tar imot friske frivillige
N/A
Prøvetakingsmetode
Ikke-sannsynlighetsprøve
Studiepopulasjon
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.
Beskrivelse
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.
Studieplan
Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.
Hvordan er studiet utformet?
Designdetaljer
Kohorter og intervensjoner
Gruppe / Kohort |
Intervensjon / Behandling |
|---|---|
|
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.
|
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.
|
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Correlation Between AI-Derived Stool Image Score and Quantitative FIT (faecal immunochemical test) Concentration
Tidsramme: Within 90 days of stool image submission
|
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
|
Sekundære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
|
Area Under the ROC Curve for FIT Positivity
Tidsramme: Within 90 days of stool image submission
|
Area under the receiver operating characteristic curve of the prespecified AI-derived stool image score for classifying participants as FIT positive or FIT negative according to the locally established FIT threshold, reported with a 95% confidence interval.
|
Within 90 days of stool image submission
|
|
Sensitivity and Specificity of the AI-Derived Stool Image Score for FIT Positivity/Negativity
Tidsramme: Within 90 days of stool image submission
|
Sensitivity and Specificity of a prespecified AI-derived stool image score threshold for identifying participants with a positive/negative FIT result, reported as a percentage with a 95% confidence interval.
|
Within 90 days of stool image submission
|
|
Area Under the ROC Curve for Colonoscopy-Detected Colorectal Neoplasia
Tidsramme: Within 90 days of stool image submission
|
Area under the receiver operating characteristic curve of the AI-derived stool image score for identifying colorectal neoplasia detected at colonoscopy, with histopathological confirmation when available.
|
Within 90 days of stool image submission
|
Samarbeidspartnere og etterforskere
Det er her du vil finne personer og organisasjoner som er involvert i denne studien.
Etterforskere
- Hovedetterforsker: Erik Manriquez Alegria, MD, Hospital Sotero Del Rio
Publikasjoner og nyttige lenker
Den som er ansvarlig for å legge inn informasjon om studien leverer frivillig disse publikasjonene. Disse kan handle om alt relatert til studiet.
Generelle publikasjoner
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Studierekorddatoer
Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.
Studer hoveddatoer
Studiestart (Faktiske)
8. mai 2026
Primær fullføring (Antatt)
1. desember 2026
Studiet fullført (Antatt)
1. desember 2026
Datoer for studieregistrering
Først innsendt
28. juli 2026
Først innsendt som oppfylte QC-kriteriene
28. juli 2026
Først lagt ut (Faktiske)
31. juli 2026
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
31. juli 2026
Siste oppdatering sendt inn som oppfylte QC-kriteriene
28. juli 2026
Sist bekreftet
1. juli 2026
Mer informasjon
Begreper knyttet til denne studien
Nøkkelord
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
- HSR-FAEX-CRC-FIT-2026
Plan for individuelle deltakerdata (IPD)
Planlegger du å dele individuelle deltakerdata (IPD)?
NEI
IPD-planbeskrivelse
Individual participant data will not be made publicly available or routinely shared with external researchers.
The dataset includes coded clinical information and potentially sensitive stool images, and access is restricted by participant consent, ethics approval, applicable data-protection requirements, and the research data-sharing agreement between Hospital Dr. Sótero del Río and Faex Health.
Study findings will be reported in aggregate and de-identified form.
Any future external secondary use would require separate institutional, ethical, legal, and data-sharing approvals.
Legemiddel- og utstyrsinformasjon, studiedokumenter
Studerer et amerikansk FDA-regulert medikamentprodukt
Nei
Studerer et amerikansk FDA-regulert enhetsprodukt
Nei
Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .