Artificial Intelligence
Conditions
Brief summary
Colorectal cancer (CRC) remains one of the leading causes of mortality among neoplastic diseases in the world\[1\] . Adequate colonoscopy based CRC screening programs have proved to be the key to reduce the risk of mortality, by early diagnosis of existing CRC and detection of pre-cancerous lesions\[2-4\] . Nevertheless, long-term effectiveness of colonoscopy is influenced by a range of variables that make it far from a perfect tool\[5\]. The effectiveness of a colonoscopy mainly depends on its quality, which in turn is dependent on the skill and expertise of the endoscopist. In fact, several studies have shown a significant adenoma miss rate of 24%-35%, especially in patients with diminutive adenomas\[6,7\] . These data are in line with interval cancers incidence (I-CRC), defined as the percentage of cancers diagnosed after a screening program and before the intended surveillance duration, of approximately 3%-5% \[8,9\]. The development of the artificial intelligence (AI) applications in the medical field has grown in interest in the past decade. Its performance on increasing automatic polyp and adenoma detection has shown promising results in order to achieve an higher ADR\[10\]. The use of computer aided diagnosis (CAD) for detection of polyps had initially been studied in ex vivo studies but in the last few years, with the advancement in computer aided technology and emergence of deep learning algorithms, use of AI during colonoscopy has been achieved and more studies have been undertaken \[10\]. Recently Fujifilm (Tokyo, Japan) has developed a new technology known as CAD-EYE aiming to support both colonic polyp detection and characterization during colonoscopy. This technology is now available in Europe, being compatible with the latest generation of Fujifilm endoscopes (ELUXEO Fujifilm Co.). However, the clinical impact of CAD-EYE system in improving the adenoma detection have yet to be assessed
Interventions
DEVICEArtificial Intelligence
Artificial intelligence
Sponsors
Istituto Clinico Humanitas
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
45 Years to No maximum
Healthy volunteers
No
Inclusion criteria
\- patients aged 45 or older undergoing average risk colonoscopy (screening) or follow-up colonoscopy for previous history of polyps (surveillance interval of 3 years or greater).
Exclusion criteria
* subjects with personal history of CRC, or IBD. * subjects affected with Lynch syndrome or Familiar Adenomatous Polyposis. * 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Adenoma per colonoscopy (APC) | 9 Months | APC, defined as the total number of histologically confirmed adenomas and carcinomas detected in the colonoscopy divided by the total number of colonoscopies. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Positive predictive value (PPV) | 9 Months | PPV, defined as the total number of histologically confirmed adenomas and carcinomas detected during the colonoscopy, divided by the total number of excisions in the colonoscopy. |
Countries
Italy
Outcome results
None listed