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  1. Koleth G, Emmanue J, Spadaccini M, Mascagni P, Khalaf K, Mori Y, et al.
    Endosc Int Open, 2022 Nov;10(11):E1474-E1480.
    PMID: 36397868 DOI: 10.1055/a-1907-6569
    Background and study aims  Artificial intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in gastroenterology while predicting its future applications. Methods  All studies registered on Clinicaltrials.gov up to November 2021 were analyzed. The studies included used AI in gastrointestinal endoscopy, inflammatory bowel disease (IBD), hepatology, and pancreatobiliary diseases. Data regarding the study field, methodology, endpoints, and publication status were retrieved, pooled, and analyzed to observe underlying temporal and geographical trends. Results  Of the 103 study entries retrieved according to our inclusion/exclusion criteria, 76 (74 %) were based on AI application to gastrointestinal endoscopy, mainly for detection and characterization of colorectal neoplasia (52/103, 50 %). Image analysis was also more frequently reported than data analysis for pancreaticobiliary (six of 10 [60 %]), liver diseases (eight of nine [89 %]), and IBD (six of eight [75 %]). Overall, 48 of 103 study entries (47 %) were interventional and 55 (53 %) observational. In 2018, one of eight studies (12.5 %) were interventional, while in 2021, 21 of 34 (61.8 %) were interventional, with an inverse ratio between observational and interventional studies during the study period. The majority of the studies were planned as single-center (74 of 103 [72 %]) and more were in Asia (45 of 103 [44 %]) and Europe (44 of 103 [43 %]). Conclusions  AI implementation in gastroenterology is dominated by computer-aided detection and characterization of colorectal neoplasia. The timeframe for translational research is characterized by a swift conversion of observational into interventional studies.
  2. Spadaccini M, Hassan C, Alfarone L, Da Rio L, Maselli R, Carrara S, et al.
    Gastrointest Endosc, 2022 Jan 04.
    PMID: 34995639 DOI: 10.1016/j.gie.2021.12.031
    BACKGROUND AND AIMS: Artificial Intelligence (AI) has been shown to be effective in polyp detection, and multiple computer-aided detection (CADe) system have been developed. False positive (FP) activation emerged as a possible way to benchmark CADe performances in clinical practice. The aim of this study is to validate a previously developed classification of FP comparing the performances of different brands of approved CADe systems.

    METHODS: We compared 2 different consecutive video libraries (40 video per arm) collected at Humanitas Research Hospital with 2 different CADe system brands (CADe A and CADe B). For each video, the number of CADe false activations, the cause and the time spent by the endoscopist to examine the area erroneously highlighted were reported. The FP activations were classified according to the previously developed classification of false positives (the NOISE classification) according to their cause and relevance.

    RESULTS: A total of 1021 FP activations were registered across the 40 videos of the Group A (25.5±12.2 FPs per colonoscopy). A comparable number of FPs were identified in the Group B (n=1028, mean:25.7±13.2 FPs per colonoscopy) (p 0.53). Among them, 22.9±9.9 (89.8%, Group A), and 22.1±10.0 (86.0%, Group B) were due to artifacts from bowel wall. Conversely, 2.6±1.9 (10.2%) and 3.5±2.1 (14%) were caused by bowel content (p 0.45). Within the Group A each false activation required 0.2±0.9 seconds, with 1.6±1.0 (6.3%) FPs requiring additional time for endoscopic assessment. Comparable results were reported within the Group B with 0.2±0.8 seconds spent per false activation and 1.8±1.2 FPs per colonoscopy requiring additional inspection.

    CONCLUSION: The use of a standardized nomenclature permitted to provide comparable results with either of the 2 recently approved CADe systems.

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