Last reviewed · How we verify

NCT05942677: AIChallengeMed

Comparison of Flat Colorectal Lesion Detection by Artificial Intelligence-assisted Colonoscopy Versus Endoscopists

Status unknown Last updated 13 November 2023
What this trial tests

trial testing proportion of colorectal lesions in Flat Colorectal Lesion in 160 participants. Status unknown.

Timeline
1 January 2022
Primary endpoint
1 June 2022
30 December 2023

Quick facts

Lead sponsorHospices Civils de Lyon
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment160
Start date1 January 2022
Primary completion1 June 2022
Estimated completion30 December 2023
Sites1 location across France

Drugs / interventions tested

Conditions studied

Sponsor

Hospices Civils de Lyon — full company profile →

Who can join

18 and older, any sex, with Flat Colorectal Lesion. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

The development of artificial intelligence (AI) systems in the field of colorectal endoscopy is currently booming, colorectal cancer being, by its frequency and severity, a real public health problem. In terms of image analysis, AI is indeed able to perform many tasks simultaneously (lesion detection, classification, and segmentation) and to combine them. Lesion detection is thus the starting point of the whole chain to choose at the end the most appropriate treatment for the patient. Large-scale studies have demonstrated the superiority of artificial intelligence-assisted detection over the usual detection by gastroenterologists, mainly for the detection of sub-centimeter polyps. However, the investigators have shown that a recent computer-aided detection system (CADe) such as the ENDO-AID software in combination with the EVIS X1 video column (Olympus, Tokyo, Japan) may present difficulties in the detection of flat lesions such as sessile serrated lesions (SSLs) and non-granular laterally spreading tumors (LST-NGs). This represents a major challenge because in addition to their shape being difficult to identify for the human eye in practice and where AI assistance would be of great value, these rare lesions are associated with advanced histology. In addition, the investigators recently described the case of a worrisome false negative of AI-assisted colonoscopy, which failed to detect a flat adenocarcinoma in the transverse colon. Therefore, it is important to measure the false negative rate of AI detection based on the macroscopic shape of the lesion. Comparing this rate to the human endoscopist's false negatives would improve the performance of AI for this specific lesion subtype in the future.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

Verify or expand the search:

Other Hospices Civils de Lyon trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT05942677.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing