Actively Recruiting
Artificial Intelligence-Based Screening Models for Personalized Prevention and Early Detection of Colorectal Cancer
Led by IRCCS Azienda Ospedaliero-Universitaria di Bologna · Updated on 2025-01-29
1400
Participants Needed
1
Research Sites
N/A
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are evaluating new personalized colorectal cancer (CRC) screening methods using artificial intelligence (AI) to improve early detection. Colorectal cancer is a common and deadly disease, with risk factors including age, obesity, smoking, diet, and physical activity. This study aims to develop a predictive risk model to tailor screening strategies, moving from a one-size-fits-all approach to personalized prevention by integrating AI and patient risk profiles. Participants will undergo colonoscopy either with traditional methods or with AI assistance using the GI Genius device to help detect colorectal lesions. Those testing positive for fecal occult blood through a regional screening program will be randomized to receive either standard colonoscopy or AI-assisted colonoscopy. Data on diet, physical activity, and other risk factors will also be collected through questionnaires. Follow-up colonoscopies may be scheduled based on findings at 1, 3, or 5 years. Participants will be involved in interviews, receive preparation instructions, complete questionnaires, and undergo colonoscopy according to their assigned group. The study will monitor adenoma detection rates and other outcomes over 18 months, with a total study duration of 72 months. Researchers will analyze data to improve screening precision and reduce unnecessary invasive procedures, aiming to optimize screening pathways based on individual risk.
CONDITIONS
Brief Title
Artificial Intelligence-based Screening Models for Prevention and Early Detection of Colorectal Cancer
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Subjects aged 50-69 years who underwent the fecal occult blood screening program
- Male and female sex
- Positive fecal occult blood test (FIT) result on immunochemical screening (OC-Sensor) with a cut-off of 20 micrograms HgB/gr of stool
- Provided written consent to participate in the study
You will not qualify if you...
- Do not understand the Italian language
- Previous history of total colectomy or bowel resections
- Colonoscopy performed in the previous 12 months
- Contraindications to colonoscopy
- Contraindications to sedation or anesthesia
- Personal history of colorectal cancer
- Hereditary gastrointestinal cancer syndromes such as familial adenomatous polyposis (FAP), attenuated FAP, MutYH-associated polyposis, Lynch or Lynch-like syndrome, and serrated polyposis syndrome
- Inflammatory bowel disease including Crohn's disease and ulcerative colitis
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Your Study Journey
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Duration - Up to 1 month
Participants who tested positive for fecal occult blood test undergo a telephone interview and outpatient clinic visits for prescription, bowel preparation, and questionnaires on diet and physical activity before colonoscopy.
1 telephone interview and 2 outpatient clinic visits (in-person)
Duration - 1 day
Participants are randomized to receive either traditional colonoscopy or colonoscopy with artificial intelligence assistance to detect colorectal lesions.
1 outpatient clinic visit (in-person) for colonoscopy and questionnaire submission
Duration - Up to 5 years
Participants with detected polyps or suspicious lesions may have follow-up colonoscopies at 1, 3, or 5 years depending on clinical findings to optimize screening pathways.
1 to 3 outpatient visits depending on clinical needs
Trial Site Locations
Total: 1 location
1
IRCCS Azienda Ospedaliero-Universitaria di Bologna
Bologna, Bologna, Italy, 40138
Actively Recruiting
Research Team
G
Giovanni Barbara, MD
C
Chiara Pierantoni, MD
How is the study designed?
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
SCREENING
Number of Arms
2
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