Colorectal Neoplasms
Conditions
Keywords
Colorectal Cancer Screening, Artificial Intelligence, Stool Image Analysis, Fecal Immunochemical Test, Colonoscopy, Digital Health
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.
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.
Interventions
DIAGNOSTIC_TESTAI-Based Stool Image Analysis
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.
Sponsors
Servicio de Salud Metropolitano Sur Oriente
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation Between AI-Derived Stool Image Score and Quantitative FIT (faecal immunochemical test) Concentration | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the ROC Curve for FIT Positivity | 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. |
| Sensitivity and Specificity of the AI-Derived Stool Image Score for FIT Positivity/Negativity | 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. |
| Area Under the ROC Curve for Colonoscopy-Detected Colorectal Neoplasia | 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. |
Countries
Chile
Contacts
CONTACTErik Manriquez Alegria, MD
CONTACTFelipe Quezada Diaz, MD
PRINCIPAL_INVESTIGATORErik Manriquez Alegria, MD
Hospital Sotero Del Rio
Outcome results
None listed