RT Journal Article
SR Electronic
T1 Deep learning in gastric tissue diseases: a systematic review
JF BMJ Open Gastroenterology
JO BMJ Open Gastro
FD BMJ Publishing Group Ltd
SP e000371
DO 10.1136/bmjgast-2019-000371
VO 7
IS 1
A1 Gonçalves, Wanderson Gonçalves e
A1 Santos, Marcelo Henrique de Paula dos
A1 Lobato, Fábio Manoel França
A1 Ribeiro-dos-Santos, Ândrea
A1 Araújo, Gilderlanio Santana de
YR 2020
UL http://bmjopengastro.bmj.com//content/7/1/e000371.abstract
AB Background In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on gastric tissues diseases, few intensive reviews are addressing this topic.Method We performed a systematic review related to applications of deep learning in gastric tissue disease analysis by digital histology, endoscopy and radiology images.Conclusions This review highlighted the high potential and shortcomings in deep learning research studies applied to gastric cancer, ulcer, gastritis and non-malignant diseases. Our results demonstrate the effectiveness of gastric tissue analysis by deep learning applications. Moreover, we also identified gaps of evaluation metrics, and image collection availability, therefore, impacting experimental reproducibility.