scholarly journals Artificial intelligence and colorectal polyp detection

2019 ◽  
Vol 2 ◽  
pp. 18-18
Author(s):  
Brandon J. Teng ◽  
Michael F. Byrne
2020 ◽  
Vol 18 (2) ◽  
pp. 200-211 ◽  
Author(s):  
Yuichi Mori ◽  
Shin-ei Kudo ◽  
Masashi Misawa ◽  
Kenichi Takeda ◽  
Toyoki Kudo ◽  
...  

Author(s):  
Giulio Antonelli ◽  
Matteo Badalamenti ◽  
Cesare Hassan ◽  
Alessandro Repici

Gut ◽  
2019 ◽  
Vol 69 (5) ◽  
pp. 799-800 ◽  
Author(s):  
Cesare Hassan ◽  
Michael B Wallace ◽  
Prateek Sharma ◽  
Roberta Maselli ◽  
Vincenzo Craviotto ◽  
...  

2019 ◽  
Vol 89 (6) ◽  
pp. AB391-AB392 ◽  
Author(s):  
Alessandro Repici ◽  
Nhan Ngo Dinh ◽  
Andrea Cherubini ◽  
Roberta Maselli ◽  
Piera Alessia Galtieri ◽  
...  

2021 ◽  
Vol 41 (01) ◽  
pp. 087-095
Author(s):  
Ingrid Chaves de Souza Borges ◽  
Natália Costa Resende Cunha ◽  
Amanda Marsiaj Rassi ◽  
Marcela Garcia de Oliveira ◽  
Jacqueline Andréia Bernardes Leão-Cordeiro ◽  
...  

Abstract Objective This metanalysis aimed to evaluate the sensitivity and specificity of computed tomography colonography in colorectal polyp detection. Methods A literature search was performed in the PubMed and Web of Science databases. Results A total of 1,872 patients (males 57.2%, females 42.8%) aged 49 to 82 years old (mean age 59.7 ± 5.3 years) were included in this metanalysis. The estimated sensitivity of computed tomography colonography was 88.4% (46.3–95.7%, coefficient of variation [CV] = 28.5%) and the estimated specificity was 73.6% (47.4–100.0%, CV = 37.5%). For lesions up to 9 mm, the sensitivity was 82.5% (62.0–99.9%, CV = 25.1%) and the specificity was 79.2% (32.0–98.0%, CV = 22.9%). For lesions > 9 mm, the sensitivity was 90.2% (64.0–100.0%, CV = 7.4%) and the specificity was 94.7% (80.0–100.0%, CV = 6.2%). No statistically significant differences in sensitivity according to the size of the lesion were found (p = 0.0958); however, the specificity was higher for lesions > 9 mm (p < 0.0001). Conclusions Most of the studies analyzed in the present work were conducted before 2010, which is about a decade after computed tomography colonography started being indicated as a screening method by European and American guidelines. Therefore, more studies aimed at analyzing the technique after further technological advancements are necessary, which could lead to the development of more modern devices.


Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265


2020 ◽  
Vol 9 (10) ◽  
pp. 3313 ◽  
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Aman Ali ◽  
...  

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.


Endoscopy ◽  
2021 ◽  
Vol 53 (09) ◽  
pp. 941-942
Author(s):  
Yuichi Mori ◽  
Michael Bretthauer

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