Colorectal NeoplasmsFECAL-AINCT07740122
AI-Based Stool Image Analysis for Colorectal Neoplasia Risk Assessment
An observational study of Colorectal Neoplasms, run by Servicio de Salud Metropolitano Sur Oriente at 1 site in Chile.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colorectal Neoplasms
- What is being tested?
- AI-Based Stool Image Analysis
- How many people
- 250 participants
- Where
- 1 siteChile
- Study dates
- 8 May 2026to Dec 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Chile1 site, 1 recruiting
- Recruiting
Hospital Sotero del Rio
Santiago, Santiago Metropolitan, 8207257
Erik Manriquez Alegria, MD, +56988232111, erik.manriquez.alegria@gmail.com
Felipe Quezada Diaz, MD, +56934651992, ffquezad@gmail.com
About this study
Official title: 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.
Read the detailed description
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.
- Conditions
- Colorectal Neoplasms
- Being tested
- AI-Based Stool Image Analysis
- Keywords
- Colorectal Cancer Screening, Artificial Intelligence, Stool Image Analysis, Fecal Immunochemical Test, Colonoscopy, Digital Health
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Stool Image AnalysisDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: AI-Based Stool Image Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Within 90 days of stool image submission
Secondary outcomes (3)
Area Under the ROC Curve for FIT Positivity
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.
Measured: Within 90 days of stool image submission
Sensitivity and Specificity of the AI-Derived Stool Image Score for FIT Positivity/Negativity
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.
Measured: Within 90 days of stool image submission
Area Under the ROC Curve for Colonoscopy-Detected Colorectal Neoplasia
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.
Measured: Within 90 days of stool image submission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Jul 2026
- Study start8 May 2026
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Colorectal NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 250 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Servicio de Salud Metropolitano Sur Oriente
- Sponsor type
- Other government
- Responsible party
- Sponsor
- Investigators
- Erik Manriquez Alegria, MD, Principal investigator, Hospital Sotero Del Rio
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07740122
- Sponsor's protocol ID
- HSR-FAEX-CRC-FIT-2026
- Acronym
- FECAL-AI
- Status last verified
- Jul 2026
- First submitted
- 28 Jul 2026
- Last updated
- 31 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Publications
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.
PubMed 39051648Collins 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.
PubMed 38626948Sounderajah 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.
PubMed 40954311Zhong 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.
PubMed 39709551Ramprasad 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.
PubMed 39866713Katsoula 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.
PubMed 28628706Rahman 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.
PubMed 37510076Bailey 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.
PubMed 33693553D'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.
PubMed 33087488
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- FECAL-AI: Prospective Observational Validation of AI-Based Stool Image Analysis Against Quantitative Fecal Immunochemical Testing for Colorectal Neoplasia Risk Assessment
- ClinicalTrials.gov ID
- NCT07740122
- Acronym / sponsor ID
- FECAL-AI / HSR-FAEX-CRC-FIT-2026
- Conditions
- Colorectal Neoplasms
- Keywords
- Colorectal Cancer Screening, Artificial Intelligence, Stool Image Analysis, Fecal Immunochemical Test, Colonoscopy, Digital Health
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Servicio de Salud Metropolitano Sur OrienteOther government
- Study start
- 8 May 2026 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 31 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Prospective Stool Image, FIT and Colonoscopy Cohort | – | Diagnostic Test: AI-Based Stool Image AnalysisAdults 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Stool Image AnalysisParticipants 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. | Diagnostic test | Prospective Stool Image, FIT and Colonoscopy Cohort |
Primary outcome
Correlation Between AI-Derived Stool Image Score and Quantitative FIT (faecal immunochemical test) Concentration
Time frame: 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.
3 secondary outcomes
Area Under the ROC Curve for FIT Positivity
Time frame: Within 90 days of stool image submission
Sensitivity and Specificity of the AI-Derived Stool Image Score for FIT Positivity/Negativity
Time frame: Within 90 days of stool image submission
Area Under the ROC Curve for Colonoscopy-Detected Colorectal Neoplasia
Time frame: Within 90 days of stool image submission
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jul 2026.
- Background: PubMed 39051648Lee 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.
- Background: PubMed 38626948Collins 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.
- Background: PubMed 40954311Sounderajah 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.
- Background: PubMed 39709551Zhong 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.
- Background: PubMed 39866713Ramprasad 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.
- Background: PubMed 28628706Katsoula 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.
- Background: PubMed 37510076Rahman 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.
- Background: PubMed 33693553Bailey 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.
- Background: PubMed 33087488D'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.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Erik Manriquez Alegria, MD principal investigator
Hospital Sotero Del Rio
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Neoplasms, run by Servicio de Salud Metropolitano Sur Oriente at 1 site in Chile.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 31 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Chile. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Erik Manriquez Alegria, MD (+56988232111, erik.manriquez.alegria@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correlation Between AI-Derived Stool Image Score and Quantitative FIT (faecal immunochemical test) Concentration (time frame: Within 90 days of stool image submission). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07740122, which also lists 9 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Jul 2026.