Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
- 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.
- 이정수; 윤지혜; 함성원; 박현정; Hyunsu Lee; 김정석; 변정식; 정훈용; 김남국; 김도훈
- Issued Date
- Artificial intelligence; Cytomegalovirus; Differential diagnosis; Endoscopy; Esophagitis; Herpes simplex; Herpes viruses; Learning algorithms; Machine learning; Transplantation
- SCIENTIFIC REPORTS
- Citation Volume
- Citation Number
- Citation Start Page
- Citation End Page
Appears in Collections:
- Medicine > Medicine
- Authorize & License
- Files in This Item:
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.