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

Repici, Alessandro; Badalamenti, Matteo; Maselli, Roberta; Correale, Loredana; Radaelli, Franco; Rondonotti, Emanuele; Ferrara, E.C.; Spadaccini, Marco; Alkandari, Asma; Fugazza, Alessandro; Anderloni, Andrea; Galtieri, Piera Alessia; Pellegatta, Gaia; Carrara, Sandro; Leo, Milena Di; Craviotto, Vincenzo; Lamonaca, Laura; Lorenzetti, Roberto; Andrealli, Alida; Antonelli, Giulio; Wallace, Michael B.; Sharma, Prateek; Rösch, Thomas; Hassan, Cesare

doi:10.1053/j.gastro.2020.04.062

Cited by 436 publications

(412 citation statements)

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“…False positive CADe outputs can be variably defined, often on the basis of duration or deemed clinical relevance making direct comparisons between trials difficult. In one trial, the false positive rate was not reported, and instead the resection rate of non-neoplastic lesions was considered which may be particularly relevant to device safety (8). CADx models can produce incorrect classifications or be designed to provide no output in cases of insufficient confidence.…”

Section: Discussionmentioning

confidence: 99%

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

et al. 2020

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Background and Aims Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers from 9 countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorised into 5 themes. Theme 1: Clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterisation, determining the optimal end-points for evaluation of AI and demonstrating impact on interval cancer rates. Theme 2: Technological Developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false positive rates and minimising latency. Theme 3: Clinical adoption/Integration (1 question) concerning effective combination of detection and characterisation into one workflow. Theme 4: Data access/annotation (1 question) concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: Regulatory Approval (1 question) related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.

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Section: Discussionmentioning

confidence: 99%

Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

et al. 2020

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“…In a validation study, GI Genius had an overall sensitivity per lesion of 99.7% and detected polyps faster than endoscopists in 82% of cases. 47 In a recent randomized controlled trial, Repici et al 48 demonstrated a 14% increase in adenoma detection rates using this CAD system.…”

Section: Ai-assisted Endoscopymentioning

confidence: 93%

History of artificial intelligence in medicine

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Enslin

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2020

Gastrointestinal Endoscopy

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“…Initial CADe studies used traditional handcrafted algorithms for image analysis 17,18 ; however, several recent publications have reported on the use of DL for polyp detection. 12,[19][20][21] A small study assessed a computer-aided polyp detection model by using 24 archived colonoscopy videos containing 31 polyps. 17 Polyp location was marked by an expert endoscopist and was used as the criterion standard.…”

Section: Applications In Endoscopymentioning

confidence: 99%