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

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Affiliated Author(s)
이현수
Alternative Author(s)
Lee, Hyun Su
Journal Title
Sci Rep
ISSN
2045-2322
Issued Date
2021
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.
Department
Dept. of Anatomy (해부학)
Publisher
School of Medicine (의과대학)
Citation
Jung Su Lee. (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
DOI
10.1038/s41598-020-78556-z
URI
http://kumel.medlib.dsmc.or.kr/handle/2015.oak/43659
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Anatomy (해부학)
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