Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection.

Autor: Lin CH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Hsu PI; Division of Gastroenterology, Department of Medicine, An Nan Hospital, China Medical University, Tainan, Taiwan., Tseng CD; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. a1102405111@gmail.com.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. a1102405111@gmail.com., Chao PJ; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Wu IT; Division of Gastroenterology, Department of Medicine, An Nan Hospital, China Medical University, Tainan, Taiwan., Ghose S; Department of Education and Research, An Nan Hospital, China Medical University, Tainan, Taiwan., Shih CA; Division of Gastroenterology and Hepatology, Department of Internal Medicine, Antai Medical Care Corporation, Antai Tian-Sheng Memorial Hospital, Donggan, Pingtung County, Taiwan.; Department of Nursing, Meiho University, Neipu, Pingtung County, Taiwan., Lee SH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Department of Radiation Oncology, Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Linkou, Taiwan., Ren JH; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan., Shie CB; Division of Gastroenterology, Department of Medicine, An Nan Hospital, China Medical University, Tainan, Taiwan., Lee TF; Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. tsairfwulee@gmail.com.; Medical Physics and Informatics Laboratory of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778, Taiwan. tsairfwulee@gmail.com.; Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan. tsairfwulee@gmail.com.; PhD Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan. tsairfwulee@gmail.com.; School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan. tsairfwulee@gmail.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Aug 17; Vol. 13 (1), pp. 13380. Date of Electronic Publication: 2023 Aug 17.
DOI: 10.1038/s41598-023-40179-5
Abstrakt: Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists' impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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