계명대학교 의학도서관 Repository

Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

Metadata Downloads
Author(s)
Jung Su LeeJihye YunSungwon HamHyunjung ParkHyunsu LeeJeongseok KimJeong-Sik ByeonHwoon-Yong JungNamkug KimDo Hoon Kim
Keimyung Author(s)
Lee, Hyun Su
Department
Dept. of Anatomy (해부학)
Journal Title
Sci Rep
Issued Date
2021
Volume
11
Abstract
The endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and hence the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. Therefore, we developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. We analyzed 87 patients with HSV esophagitis and 63 patients with CMV esophagitis and developed a machine-learning-based artificial intelligence (AI) system using a total of 666 endoscopic images with HSV esophagitis and 416 endoscopic images with CMV esophagitis. In the five repeated five-fold cross-validations based on the hue–saturation–brightness color model, logistic regression with a least absolute shrinkage and selection operation showed the best performance (sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve: 100%, 100%, 100%, 100%, 100%, and 1.0, respectively). Previous history of transplantation was included in classifiers as a clinical factor; the lower the performance of these classifiers, the greater the effect of including this clinical factor. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses.
Keimyung Author(s)(Kor)
이현수
Publisher
School of Medicine (의과대학)
Citation
Jung Su Lee et al. (2021). Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis. Sci Rep, 11, 3672. doi: 10.1038/s41598-020-78556-z
Type
Article
ISSN
2045-2322
Source
https://www.nature.com/articles/s41598-020-78556-z
DOI
10.1038/s41598-020-78556-z
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43659
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Anatomy (해부학)
공개 및 라이선스
  • 공개 구분공개
파일 목록

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.