期刊
AMERICAN JOURNAL OF OPHTHALMOLOGY
卷 228, 期 -, 页码 96-105出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ajo.2021.03.061
关键词
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资金
- National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA [R01 EY026593]
- David Brown Fund, New York, New York, USA
- Jillian M. and Lawrence A. Neubauer Foundation, New York, New York, USA
- New York Eye and Ear Foundation, New York, New York, USA
This study developed classification criteria for 25 common uveitides using machine learning techniques, achieving high overall accuracy in validation. The criteria performed well for clinical and translational research purposes.
PURPOSE: To develop classification criteria for 25 of the most common uveitides. DESIGN: Machine learning using 5,766 cases of 25 uveitides. METHODS: Cases were collected in an informatics-designed preliminary database. Using formal consensus techniques, a final database was constructed of 4,046 cases achieving supermajority agreement on the diagnosis. Cases were analyzed within uveitic class and were split into a training set and a validation set. Machine learning used multinomial logistic regression with lasso regularization on the training set to determine a parsimonious set of criteria for each disease and to minimize misclassification rates. The resulting criteria were evaluated in the validation set. Accuracy of the rules developed to express the machine learning criteria was evaluated by a masked observer in a 10% random sample of cases. RESULTS: Overall accuracy estimates by uveitic class in the validation set were as follows: anterior uveitides 96.7% (95% confidence interval [CI] 92.4, 98.6); in termediate uveitides 99.3% (95% CI 96.1, 99.9); posterior uveitides 98.0% (95% CI 94.3, 99.3); panuveitides 94.0% (95% CI 89.0, 96.8); and infectious posterior uveitides / panuveitides 93.3% (95% CI 89.1, 96.3). Accuracies of the masked evaluation of the rules were anterior uveitides 96.5% (95% CI 91.4, 98.6) intermediate uveitides 98.4% (91.5, 99.7), posterior uveitides 99.2% (95% CI 95.4, 99.9), panuveitides 98.9% (95% CI 94.3, 99.8), and infectious posterior uveitides / panuveitides 98.8% (95% CI 93.4, 99.9). CONCLUSIONS: The classification criteria for these 25 uveitides had high overall accuracy (ie, low misclassification rates) and seemed to perform well enough for use in clinical and translational research. (C) 2021 Elsevier Inc. All rights reserved.
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