Clinical feasibility of deep learning-assisted classification of Helicobacter pylori infection in endoscopic imagery: a reader study.

Seo JY, Kang J, Kim DH, Kim N.

Open source

DOI: 10.3904/kjim.2024.245
Published: 2026-09-01
Container: Korean J Intern Med
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3904/kjim.2024.245,
  title = {Clinical feasibility of deep learning-assisted classification of Helicobacter pylori infection in endoscopic imagery: a reader study.},
  author = {Seo JY and  Kang J and  Kim DH and  Kim N.},
  year = {2026},
  journal = {Korean J Intern Med},
  doi = {10.3904/kjim.2024.245},
  url = {https://doi.org/10.3904/kjim.2024.245}
}

RIS

TY  - JOUR
TI  - Clinical feasibility of deep learning-assisted classification of Helicobacter pylori infection in endoscopic imagery: a reader study.
AU  - Seo JY
AU  -  Kang J
AU  -  Kim DH
AU  -  Kim N.
PY  - 2026
JO  - Korean J Intern Med
DO  - 10.3904/kjim.2024.245
UR  - https://doi.org/10.3904/kjim.2024.245
ER  -

APA

JY, S., J, K., DH, K., & N., K. (2026). Clinical feasibility of deep learning-assisted classification of Helicobacter pylori infection in endoscopic imagery: a reader study.. Korean J Intern Med. https://doi.org/10.3904/kjim.2024.245

Source records

europe-pmc · retrieved 2026-09-26T19:13:00.689Z
doaj · retrieved 2026-09-26T19:13:00.686Z