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    Topic review

    Diagnosis of Helicobacter pylori Infection

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    Submitted by: Bing Hu
    (This entry belongs to Entry Collection "Gastrointestinal Disease ")

    Definition

    Helicobacter pylori (H. pylori) infects approximately 50% of the world population. Its infection is associated with gastropathies, extra-gastric digestive diseases, and diseases of other systems. There is a canonical process from acute-on-chronic inflammation, chronic atrophic gastritis (CAG), intestinal metaplasia (IM), dysplasia, and intraepithelial neoplasia, eventually to gastric cancer (GC). H. pylori eradication abolishes the inflammatory response and early treatment prevents the progression to preneoplastic lesions.

    1. Introduction

    Helicobacter pylori (H. pylori) infection is chronic and usually acquired in childhood. Globally, H. pylori infects an estimated 50% of the global population which is influenced by socioeconomic status, sanitation, regions, and age. For continents, it was reported that Africa had the highest prevalence of H. pylori infection (70.1%), whereas Oceania had the lowest prevalence (24.4%). For countries, the prevalence of H. pylori infection varied from as low as 18.9% in Switzerland to 87.7% in Nigeria [1]. One meta-analysis reported an overall prevalence of 44.3% involving 410,879 participants from 73 countries in six continents, with a rate of 50.8% in developing countries compared with 34.7% in developed countries, 42.7% in females compared to 46.3% in males, and 48.6% in adults (≥18 years) compared to 32.6% in children [2]. H. pylori gastritis was defined as an infectious disease and should be offered eradication therapy. If there is H. pylori-associated dyspepsia or functional dyspepsia, eradication of H. pylori is the first-line treatment. Symptoms can be attributed to H. pylori gastritis if sustained symptoms get remission after 6–12 months [3][4]. Regarding gastric cancer (GC), some potential changes caused by H. pylori infection may contribute to the progress of GC, which includes gastric dysbacteriosis [5], changing gastric mucosal, and cellular immunity as one component of inflammatory microenvironment [6][7], aberrant deoxyribonucleic acid (DNA) methylation [8], abnormal expression of ribonucleic acids (RNAs) (micro RNAs, long noncoding RNA, and messenger RNAs) [9][10], and single-nucleotide polymorphisms [11], et al. Chronic atrophic gastritis (CAG) and intestinal metaplasia (IM) are precancerous conditions in which dysplasia (neoplastic precancerous lesion) and adenocarcinoma may occur. GC incidence of mild, moderate, and severe atrophy is 0.04–0.10%/year, 0.12–0.34%/year, and 0.31–1.60%/year, respectively [12][13]. GC incidence in patient with IM is 0.038–1.708%/year, and the progressing rate to dysplasia in IM patient was estimated to be 1.251%/year [14][15]. Endoscopic assessment, H. pylori infection diagnosis, and surveillance are recommended in patients with precancerous conditions. Endoscopically visible lesion harboring low- or high-grade dysplasia or GC should undergo staging and treatment [16][17]. H. pylori eradication heals acute inflammation and nonatrophic chronic gastritis and may lead to regression of atrophic gastritis and reduce the risk of GC in patients with nonatrophic and atrophic gastritis. H. pylori eradication is recommended in patients who have family history of GC, CAG, IM, dysplasia, or cancer and in patients with gastric neoplasia or early GC after endoscopic therapy or by subtotal gastrectomy to prevent metachronous recurrence [16][17].

    2. Endoscopic Diagnosis

    2.1. Conventional White Light Imaging (WLI)

    Globally, the prevalence of gastritis is near 50%, which was shown from 40.7% to 56.0% and included 20–30% chronic atrophic gastritis. H. pylori-negative gastritis was from 17.7% to 20.5%, in which chronic gastritis accounted for 10–15% [18][19][20]. It indicates that H. pylori infection is generally consistent with the prevalence of gastritis and H. pylori-positive gastritis generally accounts for more than 80%. Therefore, it is the basis of clinical application of gastritis in Kyoto classification, as only a small proportion of gastritis may not be infected by H. pylori. Endoscopic findings of conventional white light imaging (WLI) can initially predict the status of H. pylori and the suspicious infection according to gastritis in Kyoto classification, and then biopsies are taken according to Sydney system [3][21]. Kyoto classification of gastritis including diffuse redness, regular arrangement of collecting venules (RAC), fundic gland polyp (FGP), atrophy, xanthoma, hyperplastic polyp, map-like redness, intestinal metaplasia, nodularity, mucosal swelling, white and flat elevated lesion, sticky mucus, depressive erosion, raised erosion, red streak, and enlarged folds. Regarding validation research, RAC, FGP, and red streak were demonstrated with satisfactory diagnostic odds ratios (DOR) for predicting uninfected status. Nodularity, diffuse redness, mucosal swelling, enlarged fold and sticky mucus were significantly associated with current infection. Map-like redness was responsible for past infection, and the overall diagnostic accuracy rate of Kyoto classification of gastritis was more than 80% [22][23][24][25]. Furthermore, with regard of uninfected status, one study showed RAC had excellent negative predictive value (NPV) of about 90% and sensitivity value of up to 85% [26]. A meta-analysis including 4070 patients also showed RAC was a valuable endoscopic feature of uninfected status with 0.80 sensitivity, 0.97 specificity, and 0.97 area under the curve (AUC) [27]. With regard of current infection, Kyoto classification score (including atrophy, IM, enlarged folds, nodularity, and diffuse redness) ≥2 could predict H. pylori infection with 89.7% accuracy, 78.3% sensitivity, and 92.0% specificity in patients with a high-negative titer of anti-H. pylori antibody [28]. One study showed an AUC for H. pylori infection of WLI was 0.81 in the corpus and 0.71 in the antrum and indigo carmine contrast (IC) method was useful in gastric swelling areas [29]. Other research reported 0.82–0.92 AUC used self-assembled score systems to predict H. pylori infection [30][31]. However, there are two problems that cannot be ignored in real time clinical practice. The first one is the professional level and experience, as well as interobserver agreement. A brief mini-lecture on the Kyoto Classification of Gastritis could improve the accuracy from 90.3% to 96.5% [32]. The second one is the clinical routine that biopsy rather than other detecting methods (UBT, Hp SAT, or serological test) will be taken after primary prediction via Kyoto Classification of Gastritis. From the data mentioned above, Kyoto Classification of Gastritis is more characterized with higher specificity and slightly inferior sensitivity. One clinical research reported no endoscopic features (alone or in combination) showed a sensitivity of more than 57% for H. pylori infection [33], which may further result in increasing missed diagnosis rate. The uneven distribution of H. pylori inevitably leads to sampling errors in biopsy-based examinations including rapid urease test (RUT), histology, or culture. Biopsies from multipoints can improve the accuracy of detection. Two samples (one from the antrum avoiding areas of ulceration and obvious IM and one from normal appearing corpus) can provide the highest yield for RUT, as well as time saving [34]. The sensitivity of RUT was reported to vary between 80% and 100%, and its specificity is between 97% and 99% [35]. If less than 104 bacterial cells are present in the gastric biopsy, false-negative results are obtained most probably [36]. It is essential to improve the sensitivity. Therefore, many efforts were done on newer imaging techniques such as image-enhanced endoscopy (IEE) and aiding systems such as AI.

    2.2. Image-Enhanced Endoscopy (IEE)

    IEE including magnifying endoscopy and digital chromoendoscopy such as narrow-band imaging (NBI), autofluorescence imaging (AFI), blue laser imaging (BLI), and linked color imaging (LCI) offered advantages in diagnosing H. pylori.
    Magnifying endoscopy (ME) can provide more precise information concerning the collecting venules, the network of capillaries surrounding the gastric pits, the swelling of the surface epithelium between pits, and the enlargement and destruction of the pits, which was considered useful for the diagnosis of histopathologic gastritis [37][38]. Type Z-0: subepithelial capillary network (SECN) with regular arrangement of collecting venules and gastric pits resembling pinholes. The sensitivity, specificity, positive predictive value (PPV), and NPV of the type Z-0 pattern for predicting normal gastric mucosa were 90.3–92.7%, 93.9–100%, 100%, and 83.8% [39][40][41]. Types Z-1 and 2 patterns (enlarged gastric pits, irregular or loss of SECN, and an absence of collecting venules) were reported with sensitivity, specificity, PPV, and NPV for predicting H. pylori infection were 100%, 92.7%, 83.8%, and 100% [39][40]. A meta-analysis involving 1897 patients reported the pooled sensitivity and specificity of ME to predict H. pylori infection were 0.89 and 0.82, respectively, with an AUC of 0.95 [42]. Compared with that of conventional WLI, ME can be superior for the diagnosis of H. pylori gastritis. The “pit plus vascular pattern” classification in the gastric corpus observed by ME was able to accurately predict the status of H. pylori infection with a pooled sensitivity and specificity of 0.96 and 0.91, respectively, with an AUC of 0.99 [42]. The sensitivity and specificity of irregularly arranged antral ridge pattern for the prediction of antral gastritis were 89.3–96.3% and 65.2–73.7%, respectively [41][43]. Indigo carmine staining increased sensitivity and specificity up to 97.6% and 100% for corporal gastritis, and up to 88.4% and 75.0% for antral gastritis, respectively [41].

    2.3. Electronic Chromoendoscopy

    Non-M-NBI endoscopy is an optical image enhancement technique to enhance the visualization of mucosal microscopic structure and capillaries of the superficial mucosal layer. One study firstly and retrospectively found NBI could be a promising method for H. pylori infection identification [44]. According to five gastric mucosal morphologic patterns of non-M-NBI, type 3 (rod-shaped gastric pits with prominent sulci), 4 (ground glass-like morphology), or 5 (dark brown patches with bluish margin and irregular border) morphologies were statistically significant in predicting H. pylori positive status and achieved 94.28% sensitivity, 96.66% specificity, 98.50% PPV, and 87.87% NPV [45]. A further retrospective study on the site-specific biopsy guided by NBI of abnormal mucosa rather than the random biopsy for the diagnosis of H. pylori showed higher 95.4% sensitivity and 97.3% specificity [46]. However, a multicenter prospective study demonstrated no difference in the accuracy of diagnosing H. pylori gastritis between NBI and WLI (74% NBI vs. 73% WLI), although NBI demonstrated slightly higher sensitivity (69% vs. 57) but reduced specificity (67% vs. 79%) [47].

    2.4. Linked-Color Imaging and Blue Laser Imaging

    Linked color imaging (LCI) can show mucosal color similar to WLI but produce more color patterns of the mucosa due to emission intensity at wavelengths different from WLI [48]. These colors allow endoscopists to diagnose a variety of lesions such as inflammation areas because of the high color contrast with surrounding mucosa. Blue laser imaging (BLI) is another IEE that combines narrow-spectrum blue laser with white light to make up the deficiency of NBI [49]. The push of a single button during endoscopy allows one to switch between LCI and BLI. LCI is brighter than WLI, and BLI is brighter than NBI. LCI produces particularly bright images in the stomach and is useful when screening gastric lesions, whereas BLI-bright and BLI are also useful in displaying mucosal structure and vessels in close-up views inside the stomach, as well as relatively close views, especially the antrum [50]. Some research has indicated H. pylori infection could be identified by LCI and BLI. With regard of BLI, one study included patients’ mucosal patterns observed by BLI and divided into Spotty, Cracked, and Mottled pattern groups with results of 12/77, 105/17, and 138/90 negative/positive for H. pylori infection, respectively. The specificity and PPV for endoscopic diagnosis with positive H. pylori infection based on the Spotty pattern were 95.3% and 86.5% [51]. On the aspect of LCI which is more suitable in wide-lumen organ than BLI, studies based on Kyoto Classification of Gastritis to assess the visibility of LCI, WLI, and BLI found that LCI could improve visibility especially for diffuse redness, spotty redness, map-like redness, patchy redness and red streaks [52][53][54]. When compared with that of WLI, LCI could identify H. pylori infection by enhancing endoscopic images of the diffuse redness of the fundic gland and achieve more optimal diagnostic power (accuracy 85.8% vs. 74.2%, sensitivity 93.3% vs. 81.7%, and specificity 78.3% vs. 66.7%) [55]. Another study reported that the application of LCI at the corpus to identify H. pylori infection could be reliable and superior to WLI with the highest accuracy among groups (81.2% vs. 64.3–76.5%), as well as higher sensitivity (85.41%) and specificity (79.71%) [56]. A prospective study also indicted the accuracy of LCI was higher than that of WLI (accuracy 86.6% vs. 79.5%, sensitivity 84.4% vs. 84.4%, and specificity 88.9% vs. 74.6%) [57]. When compared with ME, one study recruiting 122 patients (36 had H. pylori infection) showed that LCI could play a similar role with ME and demonstrated diagnostic abilities of H. pylori infections by LCI (78.38% accuracy, 70.97% sensitivity, 82.5% specificity, 59.46% PPV and 87.84% NPV), ME (81.98% accuracy, 81.25% sensitivity, 83.87% specificity, 64.10% PPV and 91.67% NPV), and both LCI and ME (78.38% accuracy, 80.65% sensitivity, 76.25% specificity, 57.78% PPV, and 92.42% NPV) [58].

    2.5. AI: One of Present Advances in Endoscopic Diagnosis of H. Pylori Infection

    In the field of endoscopy, the application of AI has received wide attention including gastrointestinal cancers and benign diseases based on endoscopic images, videos and histopathologic slides [59]. H. pylori infection, as a dominant cause of CAG and GC, was also detected via AI methods based on endoscopic images. One meta-analysis including 8 studies and 1719 patients (385 patients with H. pylori infection vs. 1334 controls) diagnosed by WLI, BLI, or LCI reported that the sensitivity, specificity, DOR, and AUC of AI for the prediction of H. pylori infection were 0.87, 0.86, 40, and 0.92, respectively. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images [60]. Regarding WLI, a DCNN model trained and verified by WLI of gastric antrum showed a power in diagnosing atrophic gastritis with 94% accuracy, 0.95 sensitivity, and 0.94 specificity, which were higher than those of experts [61], and AI diagnosis could be done in a considerably shorter time less than 200 s [62][63]. On the aspect of ME, a CNN system was pretrained using 1492 early gastric cancer (EGC) and 1078 H. pylori associated gastritis images from M-NBI to differentiate between EGC and gastritis and evaluated by a separate test data set (151 EGC and 107 gastritis images based on ME-NBI). Finally, it achieved a diagnostic ability with 85.3% accuracy, 95.4% sensitivity, 71.0% specificity, 82.3% PPV and 91.7% NPV, respectively, and 51.83 images/second overall test speed (0.02 s/image) [64]. In terms of LCI, a study developed a machine learning method to diagnose H. pylori infection with 87.6% accuracy, 90.4% sensitivity, 85.7% specificity, 80.9% PPV and 93.1% NPV [65]. One study developed two different CAD systems, one for LCI (LCI-CAD) and one for WLI (WLI-CAD) and achieved a comparable diagnostic accuracy to that of experienced endoscopists and a higher diagnostic accuracy of the LCI-CAD system (84.2% for uninfected, 82.5% for currently infected, and 79.2% for posteradication status) than that of WLI-CAD [66]. Another study used GoogLeNet, a 22-layer DCNN pretrained by BLI-bright and LCI and tested by 222 patients (105 H. pylori-positive) to achieve a significantly higher diagnostic ability of H. pylori infection from BLI-bright (0.96 AUC, 96.7% sensitivity, and 86.7% specificity) and LCI (0.95 AUC, 96.7% sensitivity and 83.3% specificity) than that of WLI (0.66 AUC, 66.7% sensitivity and 60.0% specificity) [67]. The research indicates that AI aiding different endoscopies to diagnose H. pylori infection can achieve acceptable accuracies in preclinical stage and more efforts in need to promote the real time endoscopic diagnosis directly in the future.

    The entry is from 10.3390/diagnostics11081305

    References

    1. Hooi, J.K.Y.; Lai, W.Y.; Ng, W.K.; Suen, M.M.Y.; Underwood, F.E.; Tanyingoh, D.; Malfertheiner, P.; Graham, D.Y.; Wong, V.W.S.; Wu, J.C.Y.; et al. Global prevalence of helicobacter pylori infection: Systematic review and meta-analysis. Gastroenterology 2017, 153, 420–429.
    2. Zamani, M.; Ebrahimtabar, F.; Zamani, V.; Miller, W.H.; Alizadeh-Navaei, R.; Shokri-Shirvani, J.; Derakhshan, M.H. Systematic review with meta-analysis: The worldwide prevalence of Helicobacter pylori infection. Aliment. Pharmacol. Ther. 2018, 47, 868–876.
    3. Sugano, K.; Tack, J.; Kuipers, E.J.; Graham, D.Y.; El-Omar, E.; Miura, S.; Haruma, K.; Asaka, M.; Uemura, N.; Malfertheiner, P. Kyoto global consensus report on Helicobacter pylorigastritis. Gut 2015, 64, 1353–1367.
    4. Chey, W.D.; Leontiadis, G.I.; Howden, C.W.; Moss, S.F. ACG clinical guideline: Treatment of Helicobacter pylori infection. Am. J. Gastroenterol. 2017, 112, 212–239.
    5. Noto, J.M.; Peek, R.M. The gastric microbiome, its interaction with Helicobacter pylori, and its potential role in the progression to stomach cancer. PLoS Pathog. 2017, 13, e1006573.
    6. Pero, R.; Brancaccio, M.; Laneri, S.; De Biasi, M.G.; Lombardo, B.; Scudiero, O. A novel view of human Helicobacter pylori infections: Interplay between microbiota and beta-defensins. Biomolecules 2019, 9, 237.
    7. Yuan, X.; Zhou, Y.; Wang, W.; Li, J.; Xie, G.; Zhao, Y.; Xu, D.; Shen, L. Activation of TLR4 signaling promotes gastric cancer progression by inducing mitochondrial ROS production. Cell Death Dis. 2013, 4, e794.
    8. Tahara, T.; Arisawa, T. DNA methylation as a molecular biomarker in gastric cancer. Epigenomics 2015, 7, 475–486.
    9. Yang, J.; Song, H.; Cao, K.; Song, J.; Zhou, J. Comprehensive analysis of Helicobacter pylori infection-associated diseases based on miRNA-mRNA interaction network. Brief. Bioinform. 2019, 20, 1492–1501.
    10. Zhang, J.; Wei, J.; Wang, Z.; Feng, Y.; Wei, Z.; Hou, X.; Xu, J.; He, Y.; Yang, D. Transcriptome hallmarks in Helicobacter pylori infection influence gastric cancer and MALT lymphoma. Epigenomics 2020, 12, 661–671.
    11. Tongtawee, T.; Bartpho, T.; Kaewpitoon, S.; Kaewpitoon, N.; Dechsukhum, C.; Leeanansaksiri, W.; Loyd, R.A.; Talabnin, K.; Matrakool, L.; Panpimanmas, S. Genetic polymorphisms in TLR1, TLR2, TLR4, and TLR10 of Helicobacter pylori-associated gastritis: A prospective cross-sectional study in Thailand. Eur. J. Cancer Prev. 2018, 27, 118–123.
    12. Shichijo, S.; Hirata, Y.; Niikura, R.; Hayakawa, Y.; Yamada, A.; Ushiku, T.; Fukayama, M.; Koike, K. Histologic intestinal metaplasia and endoscopic atrophy are predictors of gastric cancer development after Helicobacter pylori eradication. Gastrointest. Endosc. 2016, 84, 618–624.
    13. Kaji, K.; Hashiba, A.; Uotani, C.; Yamaguchi, Y.; Ueno, T.; Ohno, K.; Takabatake, I.; Wakabayashi, T.; Doyama, H.; Ninomiya, I.; et al. Grading of atrophic gastritis is useful for risk stratification in endoscopic screening for gastric cancer. Am. J. Gastroenterol. 2019, 114, 71–79.
    14. Akbari, M.; Tabrizi, R.; Kardeh, S.; Lankarani, K.B. Gastric cancer in patients with gastric atrophy and intestinal metaplasia: A systematic review and meta-analysis. PLoS ONE 2019, 14, e0219865.
    15. Spence, A.D.; Cardwell, C.R.; McMenamin, Ú.C.; Hicks, B.M.; Johnston, B.T.; Murray, L.J.; Coleman, H.G. Adenocarcinoma risk in gastric atrophy and intestinal metaplasia: A systematic review. BMC Gastroenterol. 2017, 17, 157.
    16. Pimentel-Nunes, P.; Libânio, D.; Marcos-Pinto, R.; Areia, M.; Leja, M.; Esposito, G.; Garrido, M.; Kikuste, I.; Megraud, F.; Matysiak-Budnik, T.; et al. Management of epithelial precancerous conditions and lesions in the stomach (MAPS II): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG), European Society of Pathology (ESP), and Sociedade Portuguesa de Endoscopia Digestiva (SPED) guideline update 2019. Endoscopy 2019, 51, 365–388.
    17. Banks, M.; Graham, D.; Jansen, M.; Gotoda, T.; Coda, S.; Di Pietro, M.; Uedo, N.; Bhandari, P.; Pritchard, D.M.; Kuipers, E.J.; et al. British society of gastroenterology guidelines on the diagnosis and management of patients at risk of gastric adenocarcinoma. Gut 2019, 68, 1545–1575.
    18. Shiota, S.; Thrift, A.P.; Green, L.; Shah, R.; Verstovsek, G.; Rugge, M.; Graham, D.Y.; El-Serag, H.B. Clinical manifestations of Helicobacter pylori—Negative gastritis. Clin. Gastroenterol. Hepatol. 2017, 15, 1037–1046.
    19. Genta, R.M.; Sonnenberg, A. Helicobacter-Negative gastritis: A distinct entity unrelated to Helicobacter pylori infection. Aliment. Pharmacol. Ther. 2015, 41, 218–226.
    20. Zagari, R.M.; Rabitti, S.; Greenwood, D.C.; Eusebi, L.H.; Vestito, A.; Bazzoli, F. Systematic review with meta-analysis: Diagnostic performance of the combination of pepsinogen, gastrin-17 and anti-Helicobacter pylori antibodies serum assays for the diagnosis of atrophic gastritis. Aliment. Pharmacol. Ther. 2017, 46, 657–667.
    21. Dixon, M.F.; Genta, R.M.; Yardley, J.H.; Correa, P. Classification and grading of gastritis. Am. J. Surg. Pathol. 1996, 20, 1161–1181.
    22. Watanabe, K.; Nagata, N.; Nakashima, R.; Furuhata, E.; Shimbo, T.; Kobayakawa, M.; Sakurai, T.; Imbe, K.; Niikura, R.; Yokoi, C.; et al. Predictive findings for Helicobacter pylori-uninfected, -infected and -eradicated gastric mucosa: Validation study. World J. Gastroenterol. 2013, 19, 4374–4379.
    23. Yoshii, S.; Mabe, K.; Watano, K.; Ohno, M.; Matsumoto, M.; Ono, S.; Kudo, T.; Nojima, M.; Kato, M.; Sakamoto, N. Validity of endoscopic features for the diagnosis of Helicobacter pylori infection status based on the Kyoto classification of gastritis. Dig. Endosc. 2020, 32, 74–83.
    24. Zhao, J.; Xu, S.; Gao, Y.; Lei, Y.; Zou, B.; Zhou, M.; Chang, D.; Dong, L.; Qin, B. Accuracy of endoscopic diagnosis of Helicobacter pylori based on the Kyoto classification of gastritis: A multicenter study. Front. Oncol. 2020, 10, 599218.
    25. Mao, T.; Wang, Y.; Yin, F.; Zhao, Q.; Yang, L.; Ding, X.; Tian, Z. Association of endoscopic features of gastric mucosa with Helicobacter pylori infection in Chinese patients. Gastroenterol. Res. Pract. 2016, 2016, 6539639.
    26. Ebigbo, A.; Marienhagen, J.; Messmann, H. Regular arrangement of collecting venules and the Kimura-Takemoto classification for the endoscopic diagnosis of Helicobacter pylori infection: Evaluation in a western setting. Dig. Endosc. 2021, 33, 587–591.
    27. Li, L.; Jing, J.; Gao, H.; Zhang, C.; Lou, H.; Pan, W. Regular arrangement of collecting venules under endoscopy for predicting a Helicobacter pylori-negative stomach: A systematic review and meta-analysis. Gastroenterol. Hepatol. 2021, 44, 286–292.
    28. Toyoshima, O.; Nishizawa, T.; Arita, M.; Kataoka, Y.; Sakitani, K.; Yoshida, S.; Yamashita, H.; Hata, K.; Watanabe, H.; Suzuki, H. Helicobacter pylori infection in subjects negative for high titer serum antibody. World J. Gastroenterol. 2018, 24, 1419–1428.
    29. Kato, T.; Yagi, N.; Kamada, T.; Shimbo, T.; Watanabe, H.; Ida, K. The study group for establishing endoscopic diagnosis of chronic gastritis diagnosis of Helicobacter pylori infection in gastric mucosa by endoscopic features: A multicenter prospective study. Dig. Endosc. 2013, 25, 508–518.
    30. Otani, K.; Watanabe, T.; Kosaka, S.; Matsumoto, Y.; Nakata, A.; Nadatani, Y.; Fukunaga, S.; Hosomi, S.; Tanaka, F.; Kamata, N.; et al. Utility of Kyoto classification of gastritis in subjects with a high-negative titer of anti-Helicobacter pylori antibody during a medical check-up. J. Clin. Biochem. Nutr. 2020, 67, 317–322.
    31. Inui, M.; Ohwada, S.; Inui, Y.; Kondo, Y.; Moro, A.; Sasaki, K. Evaluating the accuracy of the endoscopic ABC classification system in diagnosing Helicobacter pylori-infected gastritis. Digestion 2019, 101, 298–307.
    32. Sakae, H.; Iwamuro, M.; Okamoto, Y.; Obayashi, Y.; Baba, Y.; Hamada, K.; Gotoda, T.; Abe, M.; Kono, Y.; Kanzaki, H.; et al. Evaluation of the usefulness and convenience of the Kyoto classification of gastritis in the endoscopic diagnosis of the Helicobacter pylori infection status. Digestion 2020, 101, 771–778.
    33. Redéen, S.; Petersson, F.; Jönsson, K.-Å.; Borch, K. Relationship of gastroscopic features to histological findings in gastritis and Helicobacter Pylori infection in a general population sample. Endoscopy 2003, 35, 946–950.
    34. Moon, S.W.; Kim, T.H.; Kim, H.S.; Ju, J.-H.; Ahn, Y.J.; Jang, H.J.; Shim, S.G.; Kim, H.J.; Jung, W.T.; Lee, O.-J. United rapid urease test is superior than separate test in detecting Helicobacter pylori at the gastric antrum and body specimens. Clin. Endosc. 2012, 45, 392–396.
    35. Uotani, T.; Graham, D.Y. Diagnosis of Helicobacter pylori using the rapid urease test. Ann. Transl. Med. 2015, 3, 9.
    36. Pohl, D.; Keller, P.M.; Bordier, V.; Wagner, K. Review of current diagnostic methods and advances in Helicobacter pylori diagnostics in the era of next generation sequencing. World J. Gastroenterol. 2019, 25, 4629–4660.
    37. Nakagawa, S.; Kato, M.; Shimizu, Y.; Nakagawa, M.; Yamamoto, J.; Luis, P.A.; Kodaira, J.; Kawarasaki, M.; Takeda, H.; Sugiyama, T.; et al. Relationship between histopathologic gastritis and mucosal microvascularity: Observations with magnifying endoscopy. Gastrointest. Endosc. 2003, 58, 71–75.
    38. Yagi, K.; Honda, H.; Yang, J.M.; Nakagawa, S. Magnifying endoscopy in gastritis of the corpus. Endoscopy 2005, 37, 660–666.
    39. Yagi, K.; Nakamura, A.; Sekine, A. Comparison between magnifying endoscopy and histological, culture and urease test findings from the gastric mucosa of the corpus. Endoscopy 2002, 34, 376–381.
    40. Anagnostopoulos, G.K.; Yao, K.; Kaye, P.; Fogden, E.; Fortun, P.; Shonde, A.; Foley, S.; Sunil, S.; Atherton, J.J.; Hawkey, C.; et al. High-resolution magnification endoscopy can reliably identify normal gastric mucosa, Helicobacter pylori-associated gastritis, and gastric atrophy. Endoscopy 2007, 39, 202–207.
    41. Gonen, C.; Simsek, I.; Sarioglu, S.; Akpinar, H. Comparison of high resolution magnifying endoscopy and standard videoendoscopy for the diagnosis of Helicobacter pylori gastritis in routine clinical practice: A prospective study. Helicobacter 2009, 14, 12–21.
    42. Qi, Q.; Guo, C.; Ji, R.; Li, Z.; Zuo, X.; Li, Y. Diagnostic performance of magnifying endoscopy for Helicobacter pylori Infection: A meta-analysis. PLoS ONE 2016, 11, e0168201.
    43. Kim, S.; Haruma, K.; Ito, M.; Tanaka, S.; Yoshihara, M.; Chayama, K. Magnifying gastroendoscopy for diagnosis of histologic gastritis in the gastric antrum. Dig. Liver Dis. 2004, 36, 286–291.
    44. Alaboudy, A.A.; Elbahrawy, A.; Matsumoto, S.; Yoshizawa, A. Conventional narrow-band imaging has good correlation with histopathological severity of Helicobacter pylori gastritis. Dig. Dis. Sci. 2010, 56, 1127–1130.
    45. Tongtawee, T.; Kaewpitoon, S.; Kaewpitoon, N.; Dechsukhum, C.; Loyd, R.A.; Matrakool, L. Correlation between gastric mucosal morphologic patterns and histopathological severity of Helicobacter pylori Associated gastritis using conventional narrow band imaging gastroscopy. BioMed Res. Int. 2015, 2015, 1–7.
    46. Tongtawee, T.; Dechsukhum, C.; Leeanansaksiri, W.; Kaewpitoon, S.; Kaewpitoon, N.; Loyd, R.A.; Matrakool, L.; Panpimanmas, S. Improved detection of Helicobacter pylori infection and premalignant gastric mucosa using “site specific biopsy”: A randomized control clinical trial. Asian Pac. J. Cancer Prev. 2016, 16, 8487–8490.
    47. Pimentel-Nunes, P.; Libânio, D.; Lage, J.; Abrantes, D.; Coimbra, M.; Esposito, G.; Hormozdi, D.; Pepper, M.; Drasovean, S.; White, J.R.; et al. A multicenter prospective study of the real-time use of narrow-band imaging in the diagnosis of premalignant gastric conditions and lesions. Endoscopy 2016, 48, 723–730.
    48. Shinozaki, S.; Osawa, H.; Hayashi, Y.; Lefor, A.K.; Yamamoto, H. Linked color imaging for the detection of early gastrointestinal neoplasms. Ther. Adv. Gastroenterol. 2019, 12, 1756284819885246.
    49. Bi, Y.; Min, M.; Zhang, F.; Li, X. The characteristics of blue laser imaging and the application in diagnosis of early digestive tract cancer. Technol. Cancer Res. Treat. 2019, 18, 1533033819825877.
    50. Osawa, H.; Miura, Y.; Takezawa, T.; Ino, Y.; Khurelbaatar, T.; Sagara, Y.; Lefor, A.K.; Yamamoto, H. Linked color imaging and blue laser imaging for upper gastrointestinal screening. Clin. Endosc. 2018, 51, 513–526.
    51. Nishikawa, Y.; Ikeda, Y.; Murakami, H.; Hori, S.-I.; Hino, K.; Sasaki, C.; Nishikawa, M. Classification of atrophic mucosal patterns on Blue LASER Imaging for endoscopic diagnosis of Helicobacter pylori-related gastritis: A retrospective, observational study. PLoS ONE 2018, 13, e0193197.
    52. Takeda, T.; Asaoka, D.; Nojiri, S.; Nishiyama, M.; Ikeda, A.; Yatagai, N.; Ishizuka, K.; Hiromoto, T.; Okubo, S.; Suzuki, M.; et al. Linked color imaging and the Kyoto classification of gastritis: Evaluation of visibility and inter-rater reliability. Digestion 2020, 101, 598–607.
    53. Kitagawa, Y.; Suzuki, T.; Nankinzan, R.; Ishigaki, A.; Furukawa, K.; Sugita, O.; Hara, T.; Yamaguchi, T. Comparison of endoscopic visibility and miss rate for early gastric cancers after Helicobacter pylori eradication with white-light imaging versus linked color imaging. Dig. Endosc. 2019, 32, 769–777.
    54. Jiang, Z.-X.; Nong, B.; Liang, L.-X.; Yan, Y.-D.; Zhang, G. Differential diagnosis of Helicobacter pylori-associated gastritis with the linked-color imaging score. Dig. Liver Dis. 2019, 51, 1665–1670.
    55. Dohi, O.; Yagi, N.; Onozawa, Y.; Kimura-Tsuchiya, R.; Majima, A.; Kitaichi, T.; Horii, Y.; Suzuki, K.; Tomie, A.; Okayama, T.; et al. Linked color imaging improves endoscopic diagnosis of active Helicobacter pylori infection. Endosc. Int. Open 2016, 4, E800–E805.
    56. Wang, L.; Lin, X.-C.; Li, H.-L.; Yang, X.-S.; Zhang, L.; Li, X.; Bai, P.; Wang, Y.; Fan, X.; Ding, Y.-M. Clinical significance and influencing factors of linked color imaging technique in real-time diagnosis of active Helicobacter pylori infection. Chin. Med. J. 2019, 132, 2395–2401.
    57. Ono, S.; Dohi, O.; Yagi, N.; Sanomura, Y.; Tanaka, S.; Naito, Y.; Sakamoto, N.; Kato, M. Accuracies of endoscopic diagnosis of Helicobacter pylori-gastritis: Multicenter prospective study using white light imaging and linked color imaging. Digestion 2020, 101, 624–630.
    58. Chen, T.-H.; Hsu, C.-M.; Cheng, H.-T.; Chu, Y.-Y.; Su, M.-Y.; Hsu, J.-T.; Yeh, T.-S.; Kuo, C.-F.; Chiu, C.-T. Linked color imaging can help gastric Helicobacter pylori infection diagnosis during endoscopy. J. Chin. Med Assoc. 2018, 81, 1033–1037.
    59. Yang, H.; Hu, B. Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases. Artif. Intell. Gastrointest. Endosc. 2021, 2, 25–35.
    60. Bang, C.S.; Lee, J.J.; Baik, G.H. Artificial intelligence for the prediction of Helicobacter Pylori infection in endoscopic images: Systematic review and meta-analysis of diagnostic test accuracy. J. Med. Internet Res. 2020, 22, e21983.
    61. Zhang, Y.; Li, F.; Yuan, F.; Zhang, K.; Huo, L.; Dong, Z.; Lang, Y.; Zhang, Y.; Wang, M.; Gao, Z.; et al. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Dig. Liver Dis. 2020, 52, 566–572.
    62. Zheng, W.; Zhang, X.; Kim, J.J.; Zhu, X.; Ye, G.; Ye, B.; Wang, J.; Luo, S.; Li, J.; Yu, T.; et al. High accuracy of convolutional neural network for evaluation of Helicobacter pylori infection based on endoscopic images: Preliminary experience. Clin. Transl. Gastroenterol. 2019, 10, e00109.
    63. Shichijo, S.; Nomura, S.; Aoyama, K.; Nishikawa, Y.; Miura, M.; Shinagawa, T.; Takiyama, H.; Tanimoto, T.; Ishihara, S.; Matsuo, K.; et al. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine 2017, 25, 106–111.
    64. Horiuchi, Y.; Aoyama, K.; Tokai, Y.; Hirasawa, T.; Yoshimizu, S.; Ishiyama, A.; Yoshio, T.; Tsuchida, T.; Fujisaki, J.; Tada, T. Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig. Dis. Sci. 2020, 65, 1355–1363.
    65. Yasuda, T.; Hiroyasu, T.; Hiwa, S.; Okada, Y.; Hayashi, S.; Nakahata, Y.; Yasuda, Y.; Omatsu, T.; Obora, A.; Kojima, T.; et al. Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection. Dig. Endosc. 2020, 32, 373–381.
    66. Nakashima, H.; Kawahira, H.; Sakaki, N. Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: A single-center prospective study (with video). Gastric Cancer 2020, 23, 1033–1040.
    67. Nakashima, H. Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: A single-center prospective study. Ann. Gastroenterol. 2018, 31, 462–468.
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