Clinical trial NCT04079478: The AID Study: Artificial Intelligence for Colorectal Adenoma Detection
Official title: The AID Study: Artificial Intelligence for Colorectal Adenoma Detection
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colon Cancer
Keywords: Artificial Intelligence
Lead sponsor: Istituto Clinico Humanitas
Sponsor class: OTHER
First posted: 2019-09-06
Enrollment: 700
Sex: ALL
Minimum age (years): 40
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Based on the observed prevalence of adenomas (35%) among patients undergoing colonoscopies at our center within the last 12 months, a sample size of 322 subjects per arm could allow for a 90% power to show the non-inferiority (primary end-point) of the AI-aided arm by excluding that the one-side 95% CI will exclude a difference of 10% in favour of the standard group. Such sample size will also have a 80% power to detect as statistical significant (α=0.05; two-sided test) a 10% absolute increase in the detection rate of adenomas in the AI-aided arm (secondary end-point).

Brief summary: Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (\< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC8.

Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.


Eligibility criteria: Inclusion Criteria:

All 40-80 years-old subjects undergoing a colonoscopy.

Exclusion Criteria:

* subjects with personal history of CRC, or IBD.
* patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \> 2 in any colonic segment).
* patients with previous colonic resection.
* patients on antithrombotic therapy, precluding polyp resection.
* patients who were not able or refused to give informed written consent.

Primary outcomes:
measure: Additional diagnostic yield obtained by AI-aided colonoscopy to the yield obtained by the Standard (high-definition) colonoscopy
timeFrame: 3 Months
Secondary outcomes:
Other outcomes:
Locations:
city: Rozzano
country: Italy
facility: Endoscopy Unit, Humanitas Research Hospital
state: Milano
Study officials:
affiliation: Humanitas Research Hospital IRCCS, Rozzano-Milan
name: Alessandro Repici, MD
role: PRINCIPAL_INVESTIGATOR
References: