Novel Endoscopic Techniques for the Diagnosis of Gastric Infection: a Systematic Review and Network Meta-analysis

Overview

Journal Front Microbiol

Specialty Microbiology

Date 2024 Sep 17

PMID 39286347

Authors

Wenzhe Hao

Lin Huang

Xuejun Li

Hongyu Jia

Affiliations

Soon will be listed here.

Abstract

Objective: This study aimed to conduct a network meta-analysis to compare the diagnostic efficacy of diverse novel endoscopic techniques for detecting gastric infection.

Methods: From inception to August 2023, literature was systematically searched across Pubmed, Embase, and Web of Science databases. Cochrane's risk of bias tool assessed the methodological quality of the included studies. Data analysis was conducted using the R software, employing a ranking chart to determine the most effective diagnostic method comprehensively. Convergence analysis was performed to assess the stability of the results.

Results: The study encompassed 36 articles comprising 54 observational studies, investigating 14 novel endoscopic techniques and involving 7,230 patients diagnosed with gastric infection. Compared with the gold standard, the comprehensive network meta-analysis revealed the superior diagnostic performance of two new endoscopic techniques, Magnifying blue laser imaging endoscopy (M-BLI) and high-definition magnifying endoscopy with i-scan (M-I-SCAN). Specifically, M-BLI demonstrated the highest ranking in both sensitivity (SE) and positive predictive value (PPV), ranking second in negative predictive value (NPV) and fourth in specificity (SP). M-I-SCAN secured the top position in NPV, third in SE and SP, and fifth in PPV.

Conclusion: After thoroughly analyzing the ranking chart, we conclude that M-BLI and M-I-SCAN stand out as the most suitable new endoscopic techniques for diagnosing gastric infection.

Systematic Review Registration: https://inplasy.com/inplasy-2023-11-0051/, identifier INPLASY2023110051.

References

Glover B, Teare J, Patel N . A systematic review of the role of non-magnified endoscopy for the assessment of infection. Endosc Int Open. 2020; 8(2):E105-E114. PMC: 6976312. DOI: 10.1055/a-0999-5252. View

Bang C, Lee J, Baik G . 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(9):e21983. PMC: 7527948. DOI: 10.2196/21983. View

Glover B, Teare J, Patel N . Assessment of status by examination of gastric mucosal patterns: diagnostic accuracy of white-light endoscopy and narrow-band imaging. BMJ Open Gastroenterol. 2021; 8(1). PMC: 8344320. DOI: 10.1136/bmjgast-2021-000608. View

Bansal A, Ulusarac O, Mathur S, Sharma P . Correlation between narrow band imaging and nonneoplastic gastric pathology: a pilot feasibility trial. Gastrointest Endosc. 2008; 67(2):210-6. DOI: 10.1016/j.gie.2007.06.009. View

Chen T, Hsu C, Cheng H, Chu Y, Su M, Hsu J . Linked color imaging can help gastric Helicobacter pylori infection diagnosis during endoscopy. J Chin Med Assoc. 2018; 81(12):1033-1037. DOI: 10.1016/j.jcma.2018.03.006. View

Ozgur T, Ozkan T, Erdemir G, Ozakin C, Yerci O . The diagnostic value of endoscopic narrow band imaging in helicobacter pylori gastritis in children. Turk J Gastroenterol. 2015; 26(2):112-6. DOI: 10.5152/tjg.2015.6717. View

Jiang Z, Nong B, Liang L, Yan Y, Zhang G . Differential diagnosis of Helicobacter pylori-associated gastritis with the linked-color imaging score. Dig Liver Dis. 2019; 51(12):1665-1670. DOI: 10.1016/j.dld.2019.06.024. View

Miyaki R, Yoshida S, Tanaka S, Kominami Y, Sanomura Y, Matsuo T . A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer. J Clin Gastroenterol. 2014; 49(2):108-15. DOI: 10.1097/MCG.0000000000000104. View

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(12):e0168201. PMC: 5167261. DOI: 10.1371/journal.pone.0168201. View

10.

Dohi O, Yagi N, Majima A, Horii Y, Kitaichi T, Onozawa Y . Diagnostic ability of magnifying endoscopy with blue laser imaging for early gastric cancer: a prospective study. Gastric Cancer. 2016; 20(2):297-303. DOI: 10.1007/s10120-016-0620-6. View

11.

Huang C, Sheu B, Chung P, Yang H . Computerized diagnosis of Helicobacter pylori infection and associated gastric inflammation from endoscopic images by refined feature selection using a neural network. Endoscopy. 2004; 36(7):601-8. DOI: 10.1055/s-2004-814519. View

12.

Osawa H, Yamamoto H . Present and future status of flexible spectral imaging color enhancement and blue laser imaging technology. Dig Endosc. 2013; 26 Suppl 1:105-15. DOI: 10.1111/den.12205. View

13.

Tahara T, Shibata T, Nakamura M, Yoshioka D, Okubo M, Arisawa T . Gastric mucosal pattern by using magnifying narrow-band imaging endoscopy clearly distinguishes histological and serological severity of chronic gastritis. Gastrointest Endosc. 2009; 70(2):246-53. DOI: 10.1016/j.gie.2008.11.046. View

14.

Xia H, Talley N . Natural acquisition and spontaneous elimination of Helicobacter pylori infection: clinical implications. Am J Gastroenterol. 1997; 92(10):1780-7. View

15.

Li Y, Wang H, Chen S, Yu J, Ruan R, Jin C . Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time. Dig Liver Dis. 2023; 55(5):649-654. DOI: 10.1016/j.dld.2023.02.010. View

16.

Schardt C, Adams M, Owens T, Keitz S, Fontelo P . Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak. 2007; 7:16. PMC: 1904193. DOI: 10.1186/1472-6947-7-16. View

17.

Zhang M, Pan J, Lin J, Xu M, Zhang L, Shang R . An explainable artificial intelligence system for diagnosing infection under endoscopy: a case-control study. Therap Adv Gastroenterol. 2023; 16:17562848231155023. PMC: 9989426. DOI: 10.1177/17562848231155023. View

18.

Rouse B, Chaimani A, Li T . Network meta-analysis: an introduction for clinicians. Intern Emerg Med. 2016; 12(1):103-111. PMC: 5247317. DOI: 10.1007/s11739-016-1583-7. View

19.

Shichijo S, Endo Y, Aoyama K, Takeuchi Y, Ozawa T, Takiyama H . Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images. Scand J Gastroenterol. 2019; 54(2):158-163. DOI: 10.1080/00365521.2019.1577486. View

20.

Ji R, Li Y . Diagnosing Helicobacter pylori infection in vivo by novel endoscopic techniques. World J Gastroenterol. 2014; 20(28):9314-20. PMC: 4110562. DOI: 10.3748/wjg.v20.i28.9314. View