American journal of ophthalmology · 2021
Classification Criteria for Multifocal Choroiditis With Panuveitis.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Standardization of Uveitis Nomenclature (SUN) Working Group
Аннотация
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 seemed to perform sufficiently well for use in clinical and translational research.
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 seemed to perform sufficiently well for use in clinical and translational research.