Abstract
Background and aims
Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based
system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP)
to assess H. pylori infection by using endoscopic videos in real time.
Methods
Endoscopic data were retrospectively obtained from Zhejiang Cancer Hospital (ZJCH)
for the development, validation, and testing of the system. Stored videos from ZJCH
were used for assessing and comparing the performance of IDEA-HP with that of endoscopists.
Prospective consecutive patients undergoing esophagogastroduodenoscopy were enrolled
to assess the applicability of clinical practice. The urea breath test was used as
the gold standard for diagnosing H. pylori infection.
Results
In 100 videos, IDEA-HP achieved a similar overall accuracy of assessing H. pylori infection to that of experts (84.0% vs. 83.6% [P = 0.729]). Nevertheless, the diagnostic accuracy (84.0% vs. 74.0% [P<0.001]) and sensitivity (82.0% vs. 67.2% [P<0.001]) of IDEA-HP were significantly higher than those of the beginners. In 191
prospective consecutive patients, IDEA-HP achieved accuracy, sensitivity, and specificity
of 85.3% (95% CI: 79.0%–89.3%), 83.3% (95% CI: 72.8%–90.5%), and 85.8% (95% CI: 77.7%–91.4%),
respectively.
Conclusions
Our results show that IDEA-HP has great potential for assisting endoscopists in assessing
H. pylori infection status during actual clinical work.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Digestive and Liver DiseaseAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Working group report on schistosomes, liver flukes and Helicobacter pylori.Int J Cancer. 1995; 60: 587-589
- Helicobacter pylori infection and the development of gastric cancer.N Engl J Med. 2001; 345: 784-789
- Association between helicobacter pylori eradication and gastric cancer incidence: a systematic review and meta-analysis.Gastroenterology. 2016; 150: 1113-1124
- Helicobacter pylori eradication therapy to prevent gastric cancer in healthy asymptomatic infected individuals: systematic review and meta-analysis of randomised controlled trials.BMJ. 2014; 348: g3174
- Previous Helicobacter pylori infection-induced atrophic gastritis: a distinct disease entity in an understudied population without a history of eradication.Helicobacter. 2020; 25: e12669
Kato M., Suzuki H., Warren R., Marshall B. Endoscopic Findings of H. pylori Infection[M]. Suzuki H, Warren R, Marshall B. Helicobacter pylori. Tokyo: springer Japan, 2016:157–67. Available from: https://link.springer.com/chapter/10.1007/978-4-431-55705-0_10
- Endoscopic Kyoto classification of Helicobacter pylori infection and gastric cancer risk diagnosis.World J Gastroenterol. 2020; 26: 466-477
- Kyoto classification of gastritis[m].1st ed. Nihon Medical Center, Tokyo Japan2017
- 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
- Accuracy of endoscopic diagnosis of Helicobacter pylori infection according to level of endoscopic experience and the effect of training.BMC Gastroenterol. 2013; 13: 128
- Application of artificial intelligence to gastroenterology and hepatology.Gastroenterology. 2020; 158: 76-94
- Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis.Gastrointest Endosc. 2020; 92: 821-830
- Artificial Intelligence in endoscopy.Dig Dis Sci. 2022; 67: 1553-1572
- Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images.Scand J Gastroentero. 2019; 54: 158-163
- Application of convolutional neural networks in the diagnosis of helicobacter pylori infection based on endoscopic images.EBioMedicine. 2017; 25: 106-111
- 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
- High accuracy of convolutional neural network for evaluation of helicobacter pylori infection based on endoscopic images: preliminary experience.Clin Transl Gastroenterol. 2019; 10: e00109
- Intelligent detection endoscopic assistant: an artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time.Digest Liver Dis. 2021; 53: 216-223
- Correlation of the detection rate of upper GI cancer with artificial intelligence score: results from a multicenter trial (with video).Gastrointest Endosc. 2022; 95: 1138-1146
- 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
- Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images.Endosc Int Open. 2018; 6: E139-E144
- Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection.Dig Endosc. 2020; 32: 373-381
Article info
Publication history
Published online: March 03, 2023
Accepted:
February 10,
2023
Received:
September 18,
2022
Publication stage
In Press Corrected ProofFootnotes
Sources of support: This work was partly supported by the Zhejiang Medical and Health Science and Technology Plan Project (2021KY565).
Identification
Copyright
© 2023 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
