Browsing by Author "Areia, Miguel"

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    Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study
    (Elsevier, 2022) Areia, Miguel; Mori, Yuichi; Correale, Loredana; Repici, Alessandro; Bretthauer, Michael; Sharma, Prateek; Taveira, Filipe; Spadaccini, Marco; Antonelli, Giulio; Ebigbo, Alanna; Kudo, Shin-ei; Arribas, Julia; Barua, Ishita; Kaminski, Michal F.; Messmann, Helmut; Rex, Douglas K.; Dinis-Ribeiro, Mário; Hassan, Cesare; Medicine, School of Medicine
    Background: Artificial intelligence (AI) tools increase detection of precancerous polyps during colonoscopy and might contribute to long-term colorectal cancer prevention. The aim of the study was to investigate the incremental effect of the implementation of AI detection tools in screening colonoscopy on colorectal cancer incidence and mortality, and the cost-effectiveness of such tools. Methods: We conducted Markov model microsimulation of using colonoscopy with and without AI for colorectal cancer screening for individuals at average risk (no personal or family history of colorectal cancer, adenomas, inflammatory bowel disease, or hereditary colorectal cancer syndrome). We ran the microsimulation in a hypothetical cohort of 100 000 individuals in the USA aged 50-100 years. The primary analysis investigated screening colonoscopy with versus without AI every 10 years starting at age 50 years and finishing at age 80 years, with follow-up until age 100 years, assuming 60% screening population uptake. In secondary analyses, we modelled once-in-life screening colonoscopy at age 65 years in adults aged 50-79 years at average risk for colorectal cancer. Post-polypectomy surveillance followed the simplified current guideline. Costs of AI tools and cost for downstream treatment of screening detected disease were estimated with 3% annual discount rates. The main outcome measures included the incremental effect of AI-assisted colonoscopy versus standard (no-AI) colonoscopy on colorectal cancer incidence and mortality, and cost-effectiveness of screening projected for the average risk screening US population. Findings: In the primary analyses, compared with no screening, the relative reduction of colorectal cancer incidence with screening colonoscopy without AI tools was 44·2% and with screening colonoscopy with AI tools was 48·9% (4·8% incremental gain). Compared with no screening, the relative reduction in colorectal cancer mortality with screening colonoscopy with no AI was 48·7% and with screening colonoscopy with AI was 52·3% (3·6% incremental gain). AI detection tools decreased the discounted costs per screened individual from $3400 to $3343 (a saving of $57 per individual). Results were similar in the secondary analyses modelling once-in-life colonoscopy. At the US population level, the implementation of AI detection during screening colonoscopy resulted in yearly additional prevention of 7194 colorectal cancer cases and 2089 related deaths, and a yearly saving of US$290 million. Interpretation: Our findings suggest that implementation of AI detection tools in screening colonoscopy is a cost-saving strategy to further prevent colorectal cancer incidence and mortality.
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    Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: a meta-analysis
    (Elsevier, 2020) Hassan, Cesare; Spadaccini, Marco; Iannone, Andrea; Maselli, Roberta; Jovani, Manol; Chandrasekar, Viveksandeep Thoguluva; Antonelli, Giulio; Yu, Honggang; Areia, Miguel; Dinis-Ribeiro, Mario; Bhandari, Pradeep; Sharma, Prateek; Rex, Douglas K.; Rösch, Thomas; Wallace, Michael; Repici, Alessandro; Medicine, School of Medicine
    BACKGROUND AND AIMS One fourth of colorectal neoplasia is missed at screening colonoscopy, representing the main cause of interval colorectal cancer (CRC). Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized clinical trials (RCT) reported favourable outcomes in clinical setting. Aim of this meta-analysis was to summarise available RCTs on the performance of CADe systems in colorectal neoplasia detection. METHODS We searched MEDLINE, EMBASE and Cochrane Central databases until March 2020 for RCTs reporting diagnostic accuracy of CADe systems in detection of colorectal neoplasia. Primary outcome was pooled adenoma detection rate (ADR), Secondary outcomes were adenoma per colonoscopy (APC) according to size, morphology and location, advanced APC (AAPC), as well as polyp detection rate (PDR), Polyp-per-colonoscopy (PPC), and sessile serrated lesion per colonoscopy (SPC). We calculated risk ratios (RR), performed subgroup, and sensitivity analysis, assessed heterogeneity, and publication bias. RESULTS Overall, 5 randomized controlled trials (4354 patients), were included in the final analysis. Pooled ADR was significantly higher in the CADe groups than in the control group (791/2163, 36.6% vs 558/2191, 25.2%; RR, 1.44; 95% CI, 1.27-1.62; p<0.01; I 2:42%). APC was also higher in the CADe group compared with control (1249/2163, 0.58 vs 779/2191, 0.36; RR, 1.70; 95% CI, 1.53-1.89; p<0.01;I 2:33%). APC was higher for <5 mm (RR, 1.69; 95% CI, 1.48-1.84), 6-9 mm (RR, 1.44; 95% CI, 1.19-1.75), and >10 mm adenomas (RR, 1.46; 95% CI, 1.04-2.06), as well as for proximal (RR, 1.59; 95% CI, 1.34-1.88) and distal (RR, 1.68; 95% CI, 1.50-1.88), and for flat (RR: 1.78 95% CI 1.47-2.15) and polypoid morphology (RR, 1.54; 95% CI, 1.40-1.68). Regarding histology, CADe resulted in a higher SPC (RR, 1.52; 95% CI,1.14-2.02), whereas a nonsignificant trend for AADR was found (RR, 1.35; 95% CI, 0.74 – 2.47; p = 0.33; I 2:69%). Level of evidence for RCTs was graded moderate. CONCLUSIONS According to available evidence, the incorporation of Artificial Intelligence as aid for detection of colorectal neoplasia results in a significant increase of the detection of colorectal neoplasia, and such effect is independent from main adenoma characteristics.