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Article: Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence

TitleEndoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence
Authors
KeywordsPIT PATTERN
DIAGNOSIS
Issue Date2019
PublisherThieme Open. The Journal's web site is located at https://www.thieme-connect.de/products/ejournals/journal/10.1055/s-00025476
Citation
Endoscopy International Open, 2019, v. 7 n. 4, p. E514-E520 How to Cite?
AbstractBackground and study aims: We evaluated use of artificial intelligence (AI) assisted image classifier in determining the feasibility of curative endoscopic resection of large colonic lesion based on non-magnified endoscopic images Methods: AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic lesions. The independent validation set consisted of 567 endoscopic images from 76 colonic lesions. Histology of the resected specimens was used as gold standard. Curative endoscopic resection was defined as histology no more advanced than well-differentiated adenocarcinoma, ≤ 1 mm submucosal invasion and without lymphovascular invasion, whereas non-curative resection was defined as any lesion that could not meet the above requirements. Performance of the trained AI image classifier was compared with that of endoscopists. Results: In predicting endoscopic curative resection, AI had an overall accuracy of 85.5 %. Images from narrow band imaging (NBI) had significantly higher accuracy (94.3 % vs 76.0 %; P < 0.00001) and area under the ROC curve (AUROC) (0.934 vs 0.758; P = 0.002) than images from white light imaging (WLI). AI was superior to two junior endoscopists in terms of accuracy (85.5 % vs 61.9 % or 82.0 %, P < 0.05), AUROC (0.837 vs 0.638 or 0.717, P < 0.05) and confidence level (90.1 % vs 83.7 % or 78.3 %, P < 0.05). However, there was no statistical difference in accuracy and AUROC between AI and a senior endoscopist. Conclusions: The trained AI image classifier based on non-magnified images can accurately predict probability of curative resection of large colonic lesions and is better than junior endoscopists. NBI images have better accuracy than WLI for AI prediction.
Persistent Identifierhttp://hdl.handle.net/10722/272276
ISSN
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLui, TKL-
dc.contributor.authorWong, KKY-
dc.contributor.authorMak, LLY-
dc.contributor.authorKo, MKL-
dc.contributor.authorTsao, SKK-
dc.contributor.authorLeung, WK-
dc.date.accessioned2019-07-20T10:39:06Z-
dc.date.available2019-07-20T10:39:06Z-
dc.date.issued2019-
dc.identifier.citationEndoscopy International Open, 2019, v. 7 n. 4, p. E514-E520-
dc.identifier.issn2364-3722-
dc.identifier.urihttp://hdl.handle.net/10722/272276-
dc.description.abstractBackground and study aims: We evaluated use of artificial intelligence (AI) assisted image classifier in determining the feasibility of curative endoscopic resection of large colonic lesion based on non-magnified endoscopic images Methods: AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic lesions. The independent validation set consisted of 567 endoscopic images from 76 colonic lesions. Histology of the resected specimens was used as gold standard. Curative endoscopic resection was defined as histology no more advanced than well-differentiated adenocarcinoma, ≤ 1 mm submucosal invasion and without lymphovascular invasion, whereas non-curative resection was defined as any lesion that could not meet the above requirements. Performance of the trained AI image classifier was compared with that of endoscopists. Results: In predicting endoscopic curative resection, AI had an overall accuracy of 85.5 %. Images from narrow band imaging (NBI) had significantly higher accuracy (94.3 % vs 76.0 %; P < 0.00001) and area under the ROC curve (AUROC) (0.934 vs 0.758; P = 0.002) than images from white light imaging (WLI). AI was superior to two junior endoscopists in terms of accuracy (85.5 % vs 61.9 % or 82.0 %, P < 0.05), AUROC (0.837 vs 0.638 or 0.717, P < 0.05) and confidence level (90.1 % vs 83.7 % or 78.3 %, P < 0.05). However, there was no statistical difference in accuracy and AUROC between AI and a senior endoscopist. Conclusions: The trained AI image classifier based on non-magnified images can accurately predict probability of curative resection of large colonic lesions and is better than junior endoscopists. NBI images have better accuracy than WLI for AI prediction.-
dc.languageeng-
dc.publisherThieme Open. The Journal's web site is located at https://www.thieme-connect.de/products/ejournals/journal/10.1055/s-00025476-
dc.relation.ispartofEndoscopy International Open-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectPIT PATTERN-
dc.subjectDIAGNOSIS-
dc.titleEndoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence-
dc.typeArticle-
dc.identifier.emailLui, TKL: lkl484@hku.hk-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.emailLeung, WK: waikleung@hku.hk-
dc.identifier.authorityWong, KKY=rp01393-
dc.identifier.authorityLeung, WK=rp01479-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1055/a-0849-9548-
dc.identifier.pmid31041367-
dc.identifier.pmcidPMC6447402-
dc.identifier.hkuros299486-
dc.identifier.volume7-
dc.identifier.issue4-
dc.identifier.spageE514-
dc.identifier.epageE520-
dc.identifier.isiWOS:000464532200004-
dc.publisher.placeGermany-

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