ArticleScientific reports2021
Machine learning approach for differentiating cytomegalovirus esophagitis from herpes simplex virus esophagitis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases.Alimentary pharmacology & therapeutics · 2022Pooled it
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.JMIR medical informatics · 2025Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Viral esophagitis in non-human immunodeficiency virus patients: a case-control study.Translational gastroenterology and hepatology · 2024Article
- Prediction of malaria positivity using patients' demographic and environmental features and clinical symptoms to complement parasitological confirmation before treatment.Tropical diseases, travel medicine and vaccines · 2023Article
- Cytomegalovirus Diseases of the Gastrointestinal Tract.Viruses · 2022Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.