Faex Health stool-image study lands on ClinicalTrials.gov

Faex Health says its FECAL-AI study is now publicly registered on ClinicalTrials.gov as it evaluates whether AI features from smartphone stool images match quantitative FIT results in 250 adults in Santiago, Chile. The study adds colonoscopy and histopathology as exploratory comparisons and gives researchers a public record to review the design.

Why it matters: - Faex Health is trying to show whether smartphone stool images can generate signals that track with established colorectal screening tools. - The study could help determine whether AI-based image analysis may eventually complement, but not replace, FIT testing and physician evaluation. - Public registration makes the protocol easier for clinicians, researchers and potential partners to review.

What happened: - Faex Health announced that FECAL-AI is now registered on ClinicalTrials.gov under NCT07740122. - The prospective observational study is recruiting adults at Hospital Dr. Sótero del Río in Santiago, Chile. - Servicio de Salud Metropolitano Sur Oriente is the lead sponsor. - Dr. Erik Manriquez is the principal investigator. - The official study title is “AI-Based Stool Image Analysis for Colorectal Neoplasia Risk Assessment.”

The details: - The study is designed to enroll 250 adults undergoing colorectal cancer screening or diagnostic evaluation. - Participants capture stool images through Faex Health’s smartphone platform while they undergo standard clinical testing. - The primary analysis will compare AI-derived stool-image features with quantitative FIT measurements, including FIT positivity. - Secondary exploratory analyses will evaluate associations with colonoscopy and histopathological findings when those data are available. - Faex Health provides the stool-image capture platform and AI analysis as the study’s technology and research collaborator. - The company’s technology is being used only for research in this study. - Faex Health says the platform does not diagnose colorectal cancer, replace FIT or physician evaluation, or affect treatment decisions. - The ClinicalTrials.gov record lists the study as recruiting, with estimated enrollment of 250 participants. - The study began in May 2026 and is expected to continue through 2026 as stool images and clinical data are collected. - Faex Health says the full registered study can be found by searching NCT07740122 on ClinicalTrials.gov. - Faex Health is developing AI tools that analyze human stool images captured with a conventional smartphone. - The company says the platform is being evaluated across consumer, healthcare and clinical research applications. - Faex Health says the goal is to build evidence for how everyday stool imagery may contribute health signals over time.

Between the lines: - The design centers on validation, not diagnosis, which is important for a technology working in a sensitive clinical area. - Pairing the AI output with quantitative FIT gives the study a clear benchmark against a standard screening test. - Adding colonoscopy and histopathology as exploratory comparisons could help place the image signals in a broader clinical context. - The public registry entry gives the study a durable identifier that can be tracked beyond the company’s announcement. - The collaboration also shows how a private health-tech platform can be tested inside a public-healthcare workflow.

What's next: - Investigators will collect paired stool images and clinical test results from enrolled participants. - The next major milestone is analysis of whether AI-derived image features correlate with FIT and, exploratorily, with colonoscopy and pathology findings. - Faex Health says no conclusions about clinical performance should be drawn until the study data are fully analyzed. - If the study supports the approach, Faex Health could use the results to define where smartphone stool imaging may add value in gastrointestinal screening and monitoring pathways.

The bottom line: - FECAL-AI is a public test of whether AI can extract clinically useful information from a smartphone photo of stool, using established screening measures as the benchmark.