Browsing by Author "Mori, Yuichi"
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Item Artificial Intelligence Improves Detection at Colonoscopy: Why aren’t we all already using it?(ScienceDirect, 2022) Rex, Douglas K.; Berzin, Tyler M.; Mori, Yuichi; Medicine, School of MedicineItem Combination of Mucosa-Exposure Device and Computer-Aided Detection for Adenoma Detection During Colonoscopy: A Randomized Trial(Elsevier, 2023-07) Spadaccini, Marco; Hassan, Cesare; Rondonotti, Emanuele; Antonelli, Giulio; Andrisani, Gianluca; Lollo, Gianluca; Auriemma, Francesco; Iacopini, Federico; Facciorusso, Antonio; Maselli, Roberta; Fugazza, Alessandro; Bambina Bergna, Irene Maria; Cereatti, Fabrizio; Mangiavillano, Benedetto; Radaelli, Franco; Di Matteo, Francesco; Gross, Seth A.; Sharma, Prateek; Mori, Yuichi; Bretthauer, Michael; Rex, Douglas K.; Repici, Alessandro; Medicine, School of MedicineBackground & Aims Both computer-aided detection (CADe)-assisted and Endocuff-assisted colonoscopy have been found to increase adenoma detection. We investigated the performance of the combination of the 2 tools compared with CADe-assisted colonoscopy alone to detect colorectal neoplasias during colonoscopy in a multicenter randomized trial. Methods Men and women undergoing colonoscopy for colorectal cancer screening, polyp surveillance, or clincial indications at 6 centers in Italy and Switzerland were enrolled. Patients were assigned (1:1) to colonoscopy with the combinations of CADe (GI-Genius; Medtronic) and a mucosal exposure device (Endocuff Vision [ECV]; Olympus) or to CADe-assisted colonoscopy alone (control group). All detected lesions were removed and sent to histopathology for diagnosis. The primary outcome was adenoma detection rate (percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, advanced adenomas and serrated lesions detection rate, the rate of unnecessary polypectomies (polyp resection without histologically proven adenomas), and withdrawal time. Results From July 1, 2021 to May 31, 2022, there were 1316 subjects randomized and eligible for analysis; 660 to the ECV group, 656 to the control group). The adenoma detection rate was significantly higher in the ECV group (49.6%) than in the control group (44.0%) (relative risk, 1.12; 95% CI, 1.00–1.26; P = .04). Adenomas detected per colonoscopy were significantly higher in the ECV group (mean ± SD, 0.94 ± 0.54) than in the control group (0.74 ± 0.21) (incidence rate ratio, 1.26; 95% CI, 1.04–1.54; P = .02). The 2 groups did not differ in term of detection of advanced adenomas and serrated lesions. There was no significant difference between groups in mean ± SD withdrawal time (9.01 ± 2.48 seconds for the ECV group vs 8.96 ± 2.24 seconds for controls; P = .69) or proportion of subjects undergoing unnecessary polypectomies (relative risk, 0.89; 95% CI, 0.69–1.14; P = .38). Conclusions The combination of CADe and ECV during colonoscopy increases adenoma detection rate and adenomas detected per colonoscopy without increasing withdrawal time compared with CADe alone.Item Comparative Performance of Artificial Intelligence Optical Diagnosis Systems for Leaving in Situ Colorectal Polyps(Elsevier, 2023-03) Hassan, Cesare; Sharma, Prateek; Mori, Yuichi; Bretthauer, Michael; Rex, Douglas K.; COMBO Study Group; Repici, Alessandro; Medicine, School of MedicineItem Computer-aided detection for colorectal neoplasia in randomized and non-randomized studies(Thieme, 2024-04-23) Mori, Yuichi; Patel, Harsh K.; Repici, Alessandro; Rex, Douglas K.; Sharma, Prateek; Hassan, Cesare; Medicine, School of MedicineItem 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 MedicineBackground: 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.Item Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials(Elsevier, 2023) Mori, Yuichi; Wang, Pu; Løberg, Magnus; Misawa, Masashi; Repici, Alessandro; Spadaccini, Marco; Correale, Loredana; Antonelli, Giulio; Yu, Honggang; Gong, Dexin; Ishiyama, Misaki; Kudo, Shin-ei; Kamba, Shunsuke; Sumiyama, Kazuki; Saito, Yutaka; Nishino, Haruo; Liu, Peixi; Glissen Brown, Jeremy R.; Mansour, Nabil M.; Gross, Seth A.; Kalager, Mette; Bretthauer, Michael; Rex, Douglas K.; Sharma, Prateek; Berzin, Tyler M.; Hassan, Cesare; Medicine, School of MedicineBackground and aims: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. Methods: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. Results: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). Conclusions: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.Item Quality Assurance of Computer-Aided Detection and Diagnosis in Colonoscopy(Elsevier, 2019) Vinsard, Daniela Guerrero; Mori, Yuichi; Misawa, Masashi; Kudo, Shin-ei; Rastogi, Amit; Bagci, Ulas; Rex, Douglas K.; Wallace, Michael B.; Medicine, School of MedicineRecent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field “Deep Learning,” have direct implications for computer-aided detection and diagnosis (CADe/CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice; polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect and discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both, CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine learning based CADe/CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.Item Strengths and weaknesses of an artificial intelligence polyp detection program as assessed by a high detecting endoscopist(ScienceDirect, 2022-04-12) Rex, Douglas K.; Mori, Yuichi; Sharma, Prateek; Lahr, Rachel E.; Vemulapalli, Krishna C.; Hassan, Cesare; Medicine, School of MedicineItem Variability in Adenoma Detection Rate in Control Groups of Randomized Colonoscopy Trials(Elsevier, 2022) Hassan, Cesare; Piovani, Daniele; Spadaccini, Marco; Parigi, Tommaso; Khalaf, Kareem; Facciorusso, Antonio; Fugazza, Alessandro; Rösch, Thomas; Bretthauer, Michael; Mori, Yuichi; Sharma, Prateek; Rex, Douglas K.; Bonovas, Stefanos; Repici, Alessandro; Medicine, School of MedicineBackground: Adenoma Detection Rate (ADR) is still the main surrogate outcome parameter of screening colonoscopy, but most of the studies included mixed indications and basic ADR is quite variable. We therefore looked at the control groups in randomized ADR trials using advanced imaging or mechanical methods to find out whether indications or other factors influence ADR levels. Methods: Patients in the control groups of randomized studies on ADR increase using various methods were collected based on a systematic review; this control group had to use high-definition (HD) white-light endoscopy performed between 2008 and 2021. Random-effects meta-analysis was used to pool ADR in control groups and its 95% confidence interval [CI] according to the following parameters: clinical (indication and demographic), study setting (tandem/parallel, N° centres, sample size), and technical (type of intervention, withdrawal time). Inter-study heterogeneity was reported with I-squared statistic. Multivariable mixed-effects meta-regression was performed for potentially relevant variables. Findings: 25,304 patients from 80 studies in the respective control groups were included. ADR in control arms varied between 8.2% and 68.1% with a high degree of heterogeneity (I2 = 95.1%; random-effect pooled value: 37.5% [34.6‒40.5]). There was no difference in ADR levels between primary colonoscopy screening (12 RCTs, 15%), and mixed indications including screening/surveillance and diagnostic colonoscopy; however, FIT as an indication for colonoscopy was an independent predictor of ADR (OR: 1.6 [1.1‒2.4]). Other well known parameters were confirmed by our analysis such as age (OR: 1.038 [1.004‒1.074]) and sex (male sex: OR: 1.02 [1.01‒1.03) as well withdrawal time (OR: 1.1 [1.0‒1.1). The type of intervention (imaging vs. mechanical) had no influence, but methodological factors did: more recent year of publication and smaller sample size were associated with higher ADR. Interpretation: A high level of variability was found in the level of ADR in the controls of RCTs. With regards to indications, only FIT-based colonoscopy studies influenced basic ADR, primary colonoscopy screening appeared to be similar to other indications. Standardization for variables related to clinical, methodological, and technical parameters is required to achieve generalizability and reproducibility.