Classification Criteria for Multifocal Choroiditis With Panuveitis
| Autor: | Susan E Wittenberg, Douglas A. Jabs, Peter McCluskey, Albert T. Vitale, Antoine P. Brézin, Alan G. Palestine, Jennifer E. Thorne, Brett Trusko, Russell W. Read, Neal Oden, Ralph D. Levinson |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male medicine.medical_specialty Anterior Chamber Posterior pole Visual Acuity Article Multifocal choroiditis Machine Learning Set (abstract data type) 03 medical and health sciences 0302 clinical medicine medicine Humans 030304 developmental biology 0303 health sciences Training set business.industry Multifocal Choroiditis Panuveitis Middle Aged Confidence interval Ophthalmology 030221 ophthalmology & optometry Chorioretinal scars Female Radiology business |
| Zdroj: | Am J Ophthalmol |
| ISSN: | 0002-9394 |
| DOI: | 10.1016/j.ajo.2021.03.043 |
| Popis: | Purpose To determine classification criteria for multifocal choroiditis with panuveitis (MFCPU) DESIGN: : Machine learning of cases with MFCPU and 8 other posterior uveitides. Methods Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used on the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated on the validation set. Results One thousand sixty-eight cases of posterior uveitides, including 138 cases of MFCPU, were evaluated by machine learning. Key criteria for MFCPU included: 1) multifocal choroiditis with the predominant lesions size >125 µm in diameter; 2) lesions outside the posterior pole (with or without posterior involvement); and either 3) punched-out atrophic chorioretinal scars or 4) more than minimal mild anterior chamber and/or vitreous inflammation. Overall accuracy for posterior uveitides was 93.9% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for MFCPU were 15% in the training set and 0% in the validation set. Conclusions The criteria for MFCPU had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research. |
| Databáze: | OpenAIRE |
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