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

Jung Su Lee,Do Hoon Kim,Sungwon Ham,Hyunjung Park,Hwoon-Yong Jung,Jeong-Sik Byeon,Namkug Kim,Hyunsu Lee,Jihye Yun,Jeongseok Kim

doi:10.1038/s41598-020-78556-z

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.

Highlights

The endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and the differential diagnosis between HSV and CMV esophagitis is sometimes difficult
Histopathology with specific immunohistochemical stains (IHC) or deoxyribonucleic acid (DNA) polymerase chain reaction (PCR) using tissues are required for definitive diagnosis of HSV and CMV esophagitis[4,6]
We established an artificial intelligence (AI) system with good performance based on endoscopic images for differential diagnosis between HSV and CMV esophagitis

Summary

Introduction

The endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. We developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses. The diagnosis of viral esophagitis is based on clinical history, endoscopic features, and histopathologic features. When considering empirical antiviral agents, in immunocompromised patients, endoscopic features are important for differentiating between HSV and CMV esophagitis until a specific diagnosis is made. The endoscopic features of HSV esophagitis include typically multiple, small, discrete, shallow ulcers with bullae or vesicles; yellowish exudate; and coalescence. Image feature-based classifiers could be a better classification strategy for small d atasets[17,18]

Objectives

Methods

Results

Conclusion