Warning: Undefined array key "mm" in /www/wwwroot/www.ai-bt.com/si.php on line 10 Deprecated: trim(): Passing null to parameter #1 ($string) of type string is deprecated in /www/wwwroot/www.ai-bt.com/si.php on line 10 Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?

Literature DB >> 29100847

Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?

Michael F Byrne¹, Neal Shahidi¹, Douglas K Rex².

Abstract

Mesh：

Year: 2017 PMID： 29100847 DOI： 10.1053/j.gastro.2017.10.026

Source DB: PubMed Journal: Gastroenterology ISSN： 0016-5085 Impact factor: 22.682

Keyword Cloud
Cited

× No keyword cloud information.

13 in total

1. Human-machine collaboration: bringing artificial intelligence into colonoscopy.

Authors: Omer F Ahmad; Danail Stoyanov; Laurence B Lovat
Journal: Frontline Gastroenterol Date: 2018-10-15

2. Spatially resolved diffuse reflectance spectroscopy endoscopic sensing with custom Si photodetectors.

Authors: Ben Lariviere; Katherine S Garman; N Lynn Ferguson; Deborah A Fisher; Nan M Jokerst
Journal: Biomed Opt Express Date: 2018-02-15 Impact factor: 3.732

3. Use of image-enhanced endoscopy in the characterization of colorectal polyps: Still some ways to go.

Authors: Othman R Alharbi; Nouf S Alballa; Areej S AlRajeh; Lulwah S Alturki; Ibrahim M Alfuraih; Mouhab R Jamalaldeen; Majid A Almadi
Journal: Saudi J Gastroenterol Date: 2019 Mar-Apr Impact factor: 2.485

Review 4. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped.

Authors: Emanuele Sinagra; Matteo Badalamenti; Marcello Maida; Marco Spadaccini; Roberta Maselli; Francesca Rossi; Giuseppe Conoscenti; Dario Raimondo; Socrate Pallio; Alessandro Repici; Andrea Anderloni
Journal: World J Gastroenterol Date: 2020-10-21 Impact factor: 5.742

10. Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms.

Authors: Seong Ji Choi; Eun Sun Kim; Kihwan Choi
Journal: Sci Rep Date: 2021-03-05 Impact factor: 4.379

Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?

1. Human-machine collaboration: bringing artificial intelligence into colonoscopy.

2. Spatially resolved diffuse reflectance spectroscopy endoscopic sensing with custom Si photodetectors.

3. Use of image-enhanced endoscopy in the characterization of colorectal polyps: Still some ways to go.

Review 4. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped.

Review 5. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check.

6. Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds.

Review 7. Artificial intelligence in gastrointestinal endoscopy: The future is almost here.

8. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy.

9. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks.

10. Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms.