In Gastroenterology ; h5-index 148.0
BACKGROUND & AIMS : One fourth of colorectal neoplasias are missed during screening colonoscopies-these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high-accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy.
METHODS : We analyzed data from 685 subjects (61.32±10.2 years old; 337 women) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or work up due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at three centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 min was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time.
RESULTS : The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% CI, 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07±1.54) than in the control group (mean 0.71±1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6-9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417±101 sec for the CADe group vs 435±149 for controls; P=.1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12).
CONCLUSIONS : In a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478.
Repici Alessandro, Badalamenti Matteo, Maselli Roberta, Correale Loredana, Radaelli Franco, Rondonotti Emanuele, Ferrara Elisa, Spadaccini Marco, Alkandari Asma, Fugazza Alessandro, Anderloni Andrea, Galtieri Piera Alessia, Pellegatta Gaia, Carrara Silvia, Di Leo Milena, Craviotto Vincenzo, Lamonaca Laura, Lorenzetti Roberto, Andrealli Alida, Antonelli Giulio, Wallace Michael, Sharma Prateek, Rosch Thomas, Hassan Cesare
Adenoma per colonoscopy, Artificial Intelligence, comparison, early detection