Linked Color Imaging and the Kyoto Classification of Gastritis: Evaluation of Visibility and Inter-Rater Reliability

Takeda, Tsutomu; Asaoka, Daisuke; Nojiri, Shuko; Nishiyama, Mayu; Ikeda, Atsushi; Yatagai, Noboru; Ishizuka, Kei; Hiromoto, Takahumi; Okubo, Shoki; Suzuki, Maiko; Nakajima, Akihito; Nakatsu, Yoichi; Kohno, Hiroyuki; Akazawa, Yoichi; Nakagawa, Yuta; Izumi, Kiyotaka; Matsumoto, Keīichi; Ueyama, Hiroya; Sasaki, Hirokazu; Shimada, Yuji; Matsumoto, Kenshi; Osada, Taro; Hojo, Mariko; Kato, Mototsugu; Nagahara, Akihito

doi:10.1159/000501534

Cited by 37 publications

(23 citation statements)

References 31 publications

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“…C,D) and prospectively found that the diagnostic accuracies for intestinal metaplasia were significantly higher in LCI than in WLI . Moreover, LCI improved the visibility in diffuse redness, spotty redness, map‐like redness, patchy redness, red streaks, and an atrophic border compared with WLI, while BLI‐bright improved the visibility in intestinal metaplasia . In a cross‐sectional study for the risk of EGC after successful H. pylori eradication according to the Kyoto classification of gastritis using WLI and LCI, map‐like redness and the absence of RAC were associated with EGC after H. pylori eradication in multivariate analysis .…”

Section: Iee For Diagnosis Of Kyoto Classification Of Gastritismentioning

confidence: 97%

“…56 Moreover, LCI improved the visibility in diffuse redness, spotty redness, map-like redness, patchy redness, red streaks, and an atrophic border compared with WLI, while BLI-bright improved the visibility in intestinal metaplasia. 57 In a cross-sectional study for the risk of EGC after successful H. pylori eradication according to the Kyoto classification of gastritis using WLI and LCI, map-like redness and the absence of RAC were associated with EGC after H. pylori eradication in multivariate analysis. 35 LCI was more efficient for evaluating map-like redness than WLI, because LCI could identify map-like redness more frequently than WLI (Fig.…”

Section: Bli and Lcimentioning

confidence: 99%

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Can image‐enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

Dohi

Majima

Naito

et al. 2019

Digestive Endoscopy

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Endoscopic diagnosis of Helicobacter pylori (H. pylori) infection, the most common cause of gastric cancer, is very important to clarify high-risk patients of gastric cancer for reducing morbidity and mortality of gastric cancer. Recently, the Kyoto classification of gastritis was developed based on the endoscopic characteristics of H. pylori infection-associated gastritis for clarifying H. pylori infection status and evaluating risk factors of gastric cancer. Recently, magnifying endoscopy with narrow-band imaging (NBI) has reported benefits of the accuracy and reproducibility of endoscopic diagnosis for H. pylori-related premalignant lesions. In addition to NBI, various types of image-enhanced endoscopies (IEEs) are available including autofluorescence imaging, blue laser imaging, and linked color imaging. This review focuses on understanding the clinical applications and the corresponding evidences shown to improve the diagnosis of gastritis based on Kyoto classification using currently available advanced technologies of IEEs.

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Section: Iee For Diagnosis Of Kyoto Classification Of Gastritismentioning

confidence: 97%

Section: Bli and Lcimentioning

confidence: 99%

Can image‐enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

Dohi

Majima

Naito

et al. 2019

Digestive Endoscopy

View full text Add to dashboard Cite

show abstract

“…However, studies with IEE commonly showed increased diagnostic accuracy of premalignant or malignant lesions during endoscopic examination [ 11 , 12 ]. Previous studies with IEE also indicated the usefulness of LCI for the diagnosis of H pylori infection [ 35 , 36 ]. Although a recently published systematic review concluded that currently no established uniform findings exist for optical endoscopic diagnosis of H pylori infection [ 8 ], IEE continues to have potential for the differentiation of H pylori infection.…”

Section: Discussionmentioning

confidence: 98%

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy

Seok

2020

J Med Internet Res

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Background Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. Objective This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. Methods Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Results Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. Conclusions An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. Trial Registration PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957

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“…Dohi et al 7 reported the usefulness of LCI for diagnosing HP-positive and post-HP-eradication cases by enhancing endoscopic images of diffuse redness. Takeda et al 17 reported that LCI enhances color variation of the mucosal change caused by HP infection by checking against the Kyoto classification. We focused on these color variations as a feature value of our machine-learning system.…”

Section: Discussionmentioning

confidence: 99%

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Yasuda

Hiroyasu

Hiwa

et al. 2019

Digestive Endoscopy

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Background and Aim It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer‐aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. Methods The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP‐positive and ‐negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post‐eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis. Results Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system. Conclusions The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post‐eradication patients. By learning more images and considering a diagnosis algorithm for post‐eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.

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Linked Color Imaging and the Kyoto Classification of Gastritis: Evaluation of Visibility and Inter-Rater Reliability

Cited by 37 publications

References 31 publications

Can image‐enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

Can image‐enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

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