Literature DB >> 25264763

Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence.

Teaco Kuiper1, Yasser A Alderlieste1, Kristien M A J Tytgat1, Marije S Vlug1, Joyce A Nabuurs1, Barbara A J Bastiaansen1, Mark Löwenberg1, Paul Fockens1, Evelien Dekker1.   

Abstract

BACKGROUND AND STUDY AIMS: Endoscopic optical diagnosis can potentially replace histopathological evaluation of small colorectal lesions. The aim of this study was to evaluate diagnostic performance of WavSTAT, a novel system for automatic optical diagnosis based on laser-induced autofluorescence spectroscopy. PATIENTS AND METHODS: Consecutive patients who were scheduled for colonoscopy were included in the study. Each detected lesion with a size of ≤ 9 mm was differentiated using high resolution endoscopy (HRE) by the endoscopist, who then reported this as a low or high confidence call. Thereafter, all lesions were analyzed using WavSTAT. Histopathology was used as the reference standard. The primary outcome measures were the accuracy of WavSTAT to differentiate between adenomatous and nonadenomatous lesions, and the accuracy of an algorithm combining HRE (lesions differentiated with high confidence) and WavSTAT (all remaining lesions). The secondary outcome measure was the accuracy of on-site recommended surveillance intervals.
RESULTS: At total of 87 patients with 207 small colorectal lesions were evaluated. Accuracy and negative predictive value of WavSTAT were 74.4 % and 73.5 %, respectively. The corresponding figures for the algorithm were 79.2 % and 73.9 %, respectively. Accuracy of on-site recommended surveillance interval was 73.7 % for WavSTAT alone and 77.2 % for the algorithm of HRE and WavSTAT.
CONCLUSIONS: Both accuracy of WavSTAT alone and the algorithm combining HRE with WavSTAT proved to be insufficient for the in vivo differentiation of small colorectal lesions, and do not fulfill American Society for Gastrointestinal Endoscopy performance thresholds for assessment of diminutive lesions. Future studies should assess whether combining WavSTAT with more advanced imaging techniques could result in a higher accuracy.Netherlands Trial Registry (NTR 3235). © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2014        PMID: 25264763     DOI: 10.1055/s-0034-1378112

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


  16 in total

Review 1.  Review: in vivo optical spectral tissue sensing-how to go from research to routine clinical application?

Authors:  Lisanne L de Boer; Jarich W Spliethoff; Henricus J C M Sterenborg; Theo J M Ruers
Journal:  Lasers Med Sci       Date:  2016-12-02       Impact factor: 3.161

Review 2.  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

Review 3.  Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy.

Authors:  Yu Kamitani; Kouichi Nonaka; Hajime Isomoto
Journal:  J Clin Med       Date:  2022-05-22       Impact factor: 4.964

Review 4.  Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy.

Authors:  Scott B Minchenberg; Trent Walradt; Jeremy R Glissen Brown
Journal:  World J Gastrointest Oncol       Date:  2022-05-15

5.  Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy.

Authors:  Eladio Rodriguez-Diaz; Lisa I Jepeal; György Baffy; Wai-Kit Lo; Hiroshi MashimoMD; Ousama A'amar; Irving J Bigio; Satish K Singh
Journal:  Dig Dis Sci       Date:  2021-03-24       Impact factor: 3.199

Review 6.  Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues.

Authors:  Jonas Wizenty; Teresa Schumann; Donna Theil; Martin Stockmann; Johann Pratschke; Frank Tacke; Felix Aigner; Tilo Wuensch
Journal:  Molecules       Date:  2020-04-30       Impact factor: 4.411

7.  Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology.

Authors:  Min Min; Song Su; Wenrui He; Yiliang Bi; Zhanyu Ma; Yan Liu
Journal:  Sci Rep       Date:  2019-02-27       Impact factor: 4.379

Review 8.  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

Review 9.  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 10.  Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm.

Authors:  Kyeong Ok Kim; Eun Young Kim
Journal:  Gut Liver       Date:  2021-05-15       Impact factor: 4.519

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