Literature DB >> 27285900

Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps.

Glòria Fernández-Esparrach1, Jorge Bernal2, Maria López-Cerón1, Henry Córdova1, Cristina Sánchez-Montes1, Cristina Rodríguez de Miguel1, Francisco Javier Sánchez2.   

Abstract

BACKGROUND AND AIMS: Polyp miss-rate is a drawback of colonoscopy that increases significantly for small polyps. We explored the efficacy of an automatic computer-vision method for polyp detection.
METHODS: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps that represent the likelihood of the presence of a polyp.
RESULTS: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. The mean of the maximum values on the energy map was higher for frames with polyps than without (P < 0.001). Performance improved in high quality frames (AUC = 0.79 [95 %CI 0.70 - 0.87] vs. 0.75 [95 %CI 0.66 - 0.83]). With 3.75 set as the maximum threshold value, sensitivity and specificity for the detection of polyps were 70.4 % (95 %CI 60.3 % - 80.8 %) and 72.4 % (95 %CI 61.6 % - 84.6 %), respectively.
CONCLUSION: Energy maps performed well for colonic polyp detection, indicating their potential applicability in clinical practice. © Georg Thieme Verlag KG Stuttgart · New York.

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Mesh:

Year:  2016        PMID: 27285900     DOI: 10.1055/s-0042-108434

Source DB:  PubMed          Journal:  Endoscopy        ISSN: 0013-726X            Impact factor:   10.093


  26 in total

Review 1.  Computer-aided diagnosis for colonoscopy.

Authors:  Yuichi Mori; Shin-Ei Kudo; Tyler M Berzin; Masashi Misawa; Kenichi Takeda
Journal:  Endoscopy       Date:  2017-05-24       Impact factor: 10.093

2.  Higher rate of colon polyp detection aided by an artificial intelligent software.

Authors:  Masaaki Iwatsuki; Kazuto Harada; Hideo Baba; Jaffer A Ajani
Journal:  Transl Gastroenterol Hepatol       Date:  2018-12-24

Review 3.  Optimizing Screening Colonoscopy: Strategies and Alternatives.

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

4.  Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.

Authors:  Ryan W Stidham
Journal:  Gastroenterol Hepatol (N Y)       Date:  2020-07

Review 5.  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

6.  Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning.

Authors:  Yan Wang; Zixuan Feng; Liping Song; Xiangbin Liu; Shuai Liu
Journal:  Comput Math Methods Med       Date:  2021-07-03       Impact factor: 2.238

Review 7.  Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review.

Authors:  Vatsal Patel; Marium N Khan; Aman Shrivastava; Kamran Sadiq; S Asad Ali; Sean R Moore; Donald E Brown; Sana Syed
Journal:  J Pediatr Gastroenterol Nutr       Date:  2020-01       Impact factor: 3.288

Review 8.  Artificial Intelligence in Endoscopy.

Authors:  Yutaka Okagawa; Seiichiro Abe; Masayoshi Yamada; Ichiro Oda; Yutaka Saito
Journal:  Dig Dis Sci       Date:  2021-06-21       Impact factor: 3.199

Review 9.  Artificial intelligence in inflammatory bowel disease endoscopy: current landscape and the road ahead.

Authors:  Suneha Sundaram; Tenzin Choden; Mark C Mattar; Sanjal Desai; Madhav Desai
Journal:  Ther Adv Gastrointest Endosc       Date:  2021-07-14

Review 10.  Potential for Standardization and Automation for Pathology and Endoscopy in Inflammatory Bowel Disease.

Authors:  Sana Syed; Ryan W Stidham
Journal:  Inflamm Bowel Dis       Date:  2020-09-18       Impact factor: 7.290

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