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

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 be...

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Autores principales:	Jung Su Lee, Jihye Yun, Sungwon Ham, Hyunjung Park, Hyunsu Lee, Jeongseok Kim, Jeong-Sik Byeon, Hwoon-Yong Jung, Namkug Kim, Do Hoon Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3259029cd66742028ee1e8881a139246
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spelling	oai:doaj.org-article:3259029cd66742028ee1e8881a1392462021-12-02T14:26:54ZMachine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis10.1038/s41598-020-78556-z2045-2322https://doaj.org/article/3259029cd66742028ee1e8881a1392462021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78556-zhttps://doaj.org/toc/2045-2322Abstract 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.Jung Su LeeJihye YunSungwon HamHyunjung ParkHyunsu LeeJeongseok KimJeong-Sik ByeonHwoon-Yong JungNamkug KimDo Hoon KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jung Su Lee Jihye Yun Sungwon Ham Hyunjung Park Hyunsu Lee Jeongseok Kim Jeong-Sik Byeon Hwoon-Yong Jung Namkug Kim Do Hoon Kim Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
description	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.
format	article
author	Jung Su Lee Jihye Yun Sungwon Ham Hyunjung Park Hyunsu Lee Jeongseok Kim Jeong-Sik Byeon Hwoon-Yong Jung Namkug Kim Do Hoon Kim
author_facet	Jung Su Lee Jihye Yun Sungwon Ham Hyunjung Park Hyunsu Lee Jeongseok Kim Jeong-Sik Byeon Hwoon-Yong Jung Namkug Kim Do Hoon Kim
author_sort	Jung Su Lee
title	Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
title_short	Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
title_full	Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
title_fullStr	Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
title_full_unstemmed	Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
title_sort	machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3259029cd66742028ee1e8881a139246
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Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis

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