An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning.

Autor:	Yen HH; Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan.; Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 500, Taiwan.; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 400, Taiwan., Tsai HY; Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan., Wang CC; Institute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan.; Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan., Tsai MC; Institute of Medicine, Chung Shan Medical University, Taichung 402, Taiwan.; Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan., Tseng MH; Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan.; Information Technology Office, Chung Shan Medical University Hospital, Taichung 402, Taiwan.
Jazyk:	angličtina
Zdroj:	Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2022 Nov 17; Vol. 12 (11). Date of Electronic Publication: 2022 Nov 17.
DOI:	10.3390/diagnostics12112827
Abstrakt:	Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured.
Databáze:	MEDLINE
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