Literature DB >> 25952076

Polyp-Alert: near real-time feedback during colonoscopy.

Yi Wang1, Wallapak Tavanapong2, Johnny Wong1, Jung Hwan Oh3, Piet C de Groen4.   

Abstract

We present a software system called "Polyp-Alert" to assist the endoscopist find polyps by providing visual feedback during colonoscopy. Polyp-Alert employs our previous edge-cross-section visual features and a rule-based classifier to detect a polyp edge-an edge along the contour of a polyp. The technique employs tracking of detected polyp edge(s) to group a sequence of images covering the same polyp(s) as one polyp shot. In our experiments, the software correctly detected 97.7% (42 of 43) of polyp shots on 53 randomly selected video files of entire colonoscopy procedures. However, Polyp-Alert incorrectly marked only 4.3% of a full-length colonoscopy procedure as showing a polyp when they do not. The test data set consists of about 18 h worth of video data from Olympus and Fujinon endoscopes. The technique is extensible to other brands of colonoscopes. Furthermore, Polyp-Alert can provide as high as ten feedbacks per second for a smooth display of feedback. The performance of our system is by far the most promising to potentially assist the endoscopist find more polyps in clinical practice during a routine screening colonoscopy.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Colonoscopy; Medical imaging/video; Near Real-time; Polyp detection

Mesh:

Year:  2015        PMID: 25952076     DOI: 10.1016/j.cmpb.2015.04.002

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  17 in total

Review 1.  Optimizing Screening Colonoscopy: Strategies and Alternatives.

Authors:  Hans-Dieter Allescher; Vincens Weingart
Journal:  Visc Med       Date:  2019-07-09

Review 2.  Artificial Intelligence and Polyp Detection.

Authors:  Nicholas Hoerter; Seth A Gross; Peter S Liang
Journal:  Curr Treat Options Gastroenterol       Date:  2020-01-21

Review 3.  Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective.

Authors:  Sebastian Manuel Milluzzo; Paola Cesaro; Leonardo Minelli Grazioli; Nicola Olivari; Cristiano Spada
Journal:  Clin Endosc       Date:  2021-01-13

4.  Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification.

Authors:  Eduardo Ribeiro; Andreas Uhl; Georg Wimmer; Michael Häfner
Journal:  Comput Math Methods Med       Date:  2016-10-26       Impact factor: 2.238

5.  Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.

Authors:  Masayoshi Yamada; Yutaka Saito; Hitoshi Imaoka; Masahiro Saiko; Shigemi Yamada; Hiroko Kondo; Hiroyuki Takamaru; Taku Sakamoto; Jun Sese; Aya Kuchiba; Taro Shibata; Ryuji Hamamoto
Journal:  Sci Rep       Date:  2019-10-08       Impact factor: 4.379

6.  AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices.

Authors:  Carmen C Y Poon; Yuqi Jiang; Ruikai Zhang; Winnie W Y Lo; Maggie S H Cheung; Ruoxi Yu; Yali Zheng; John C T Wong; Qing Liu; Sunny H Wong; Tony W C Mak; James Y W Lau
Journal:  NPJ Digit Med       Date:  2020-05-18

Review 7.  Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check.

Authors:  Prajna Anirvan; Dinesh Meher; Shivaram P Singh
Journal:  Euroasian J Hepatogastroenterol       Date:  2020 Jul-Dec

8.  Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN.

Authors:  Jie Meng; Linyan Xue; Ying Chang; Jianguang Zhang; Shilong Chang; Kun Liu; Shuang Liu; Bangmao Wang; Kun Yang
Journal:  Open Life Sci       Date:  2020-08-14       Impact factor: 0.938

Review 9.  Artificial intelligence in gastrointestinal endoscopy: The future is almost here.

Authors:  Muthuraman Alagappan; Jeremy R Glissen Brown; Yuichi Mori; Tyler M Berzin
Journal:  World J Gastrointest Endosc       Date:  2018-10-16

10.  HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

Authors:  Hanna Borgli; Vajira Thambawita; Pia H Smedsrud; Steven Hicks; Debesh Jha; Sigrun L Eskeland; Kristin Ranheim Randel; Konstantin Pogorelov; Mathias Lux; Duc Tien Dang Nguyen; Dag Johansen; Carsten Griwodz; Håkon K Stensland; Enrique Garcia-Ceja; Peter T Schmidt; Hugo L Hammer; Michael A Riegler; Pål Halvorsen; Thomas de Lange
Journal:  Sci Data       Date:  2020-08-28       Impact factor: 6.444

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