White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice.

Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States). Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD).

Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study.

Background: Optical diagnosis of colonic polyps is poorly reproducible outside of high volume referral centers. The present study aimed to assess whether real-time artificial intelligence (AI)-assisted optical diagnosis is accurate enough to implement the leave-in-situ strategy for diminutive (≤ 5 mm) rectosigmoid polyps (DRSPs).

Methods: Consecutive colonoscopy outpatients with ≥ 1 DRSP were included.

Endoscopic technological innovations for neoplasia detection in organized colorectal cancer screening programs: a systematic review and meta-analysis.

Background And Aims: Many endoscopic technological innovations have claimed to increase the adenoma detection rate (ADR), but their role in population-based organized screening programs is debated.

Methods: We searched PubMed, EMBASE, and Cochrane Library databases through January 2020 for randomized controlled trials (RCTs) evaluating the role of technological innovations in fecal immunochemical test (FIT)/fecal occult blood test+ subjects. The primary outcome was ADR, and secondary outcomes were advanced ADR, proximal colon ADR, mean adenoma per procedure (MAP), and cancer detection rate.

Computer-aided detection-assisted colonoscopy: classification and relevance of false positives.

Background And Aims: False positive (FP) results by computer-aided detection (CADe) hamper the efficiency of colonoscopy by extending examination time. Our aim was to develop a classification of the causes and clinical relevance of CADe FPs, and to assess the relative distribution of FPs in a real-life setting.

Methods: In a post-hoc analysis of a randomized trial comparing colonoscopy with and without CADe (NCT: 04079478), we extracted 40 CADe colonoscopy videos.

Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial.

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.