A global pandemic emerged in December 2019 from a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).¹ Phylogenetics of this virus indicate that SARS-CoV-2 is a single-stranded positive-sense RNA virus, has 79.5% homology with SARS-CoV, and is closely related to bat-derived SARS-like coronaviruses.² A recent large case series of 72 314 infected individuals in China showed an estimated 14% with severe disease and a case-fatality rate of 2.3%.^3,4

Artificial intelligence (AI) may detect colorectal polyps that have been missed due to perceptual pitfalls. By reducing such miss rate, AI may increase the detection of colorectal neoplasia leading to a higher degree of colorectal cancer (CRC) prevention. Patients undergoing CRC screening or surveillance were enrolled in 8 centers (Italy, UK, US), and randomized (1:1) to undergo 2 same-day, back-to-back colonoscopies with or without AI (deep learning computer aided diagnosis endoscopy) in 2 different arms, namely AI followed by colonoscopy without AI or vice-versa. Adenoma miss rate (AMR) was calculated as the number of histologically verified lesions detected at second colonoscopy divided by the total number of lesions detected at first and second colonoscopy. Mean number of lesions detected in the second colonoscopy and proportion of false negative subjects (no lesion at first colonoscopy and at least 1 at second) were calculated. Odds ratios (ORs) and 95% confidence intervals (CIs) were adjusted by endoscopist, age, sex, and indication for colonoscopy. Adverse events were also measured. A total of 230 subjects (116 AI first, 114 standard colonoscopy first) were included in the study analysis. AMR was 15.5% (38 of 246) and 32.4% (80 of 247) in the arm with AI and non-AI colonoscopy first, respectively (adjusted OR, 0.38; 95% CI, 0.23-0.62). In detail, AMR was lower for AI first for the ≤5 mm (15.9% vs 35.8%; OR, 0.34; 95% CI, 0.21-0.55) and nonpolypoid lesions (16.8% vs 45.8%; OR, 0.24; 95% CI, 0.13-0.43), and it was lower both in the proximal (18.3% vs 32.5%; OR, 0.46; 95% CI, 0.26-0.78) and distal colon (10.8% vs 32.1%; OR, 0.25; 95% CI, 0.11-0.57). Mean number of adenomas at second colonoscopy was lower in the AI-first group as compared with non-AI colonoscopy first (0.33 ± 0.63 vs 0.70 ± 0.97, P < .001). False negative rates were 6.8% (3 of 44 patients) and 29.6% (13 of 44) in the AI and non-AI first arms, respectively (OR, 0.17; 95% CI, 0.05-0.67). No difference in the rate of adverse events was found between the 2 groups. AI resulted in an approximately 2-fold reduction in miss rate of colorectal neoplasia, supporting AI-benefit in reducing perceptual errors for small and subtle lesions at standard colonoscopy. gov, Number: NCT03954548.

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. 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. 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. 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. European Commission and Japan Society of Promotion of Science.