Classification criteria for acute posterior multifocal placoid pigment epitheliopathy
| Autor: | Susan E Wittenberg, Brett Trusko, Andrew D. Dick, Albert T. Vitale, Alan G. Palestine, Jennifer E. Thorne, Peter McCluskey, Douglas A. Jabs, Neal Oden, Lyndell L Lim, Ralph D. Levinson, Antoine P. Brézin |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male medicine.medical_specialty Fundus Oculi Visual Acuity Stain Article Machine Learning 03 medical and health sciences Young Adult 0302 clinical medicine Oct angiography Medicine Humans Fluorescein Angiography Pigment Epithelium of Eye 030304 developmental biology 0303 health sciences Training set medicine.diagnostic_test White Dot Syndromes business.industry Choroid Acute posterior multifocal placoid pigment epitheliopathy Fluorescein angiography Confidence interval Ophthalmology 030221 ophthalmology & optometry Female Radiology business Tomography Optical Coherence |
| Zdroj: | Am J Ophthalmol |
| Popis: | Purpose To determine classification criteria for acute posterior multifocal placoid pigment epitheliopathy (APMPPE). Design Machine learning of cases with APMPPE 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 infectious posterior/panuveitides. The resulting criteria were evaluated on the validation set. Results One thousand sixty-eight cases of posterior uveitides, including 82 cases of APMPPE, were evaluated by machine learning. Key criteria for APMPPE included: 1) choroidal lesions with a plaque-like or placoid appearance and 2) characteristic imaging on fluorescein angiography (lesions "block early and stain late diffusely"). Overall accuracy for posterior uveitides was 92.7% in the training set and 98.0% (95% confidence interval 94.3, 99.3) in the validation set. The misclassification rates for APMPPE were 5% in the training set and 0% in the validation set. Conclusions The criteria for APMPPE had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research. |
| Databáze: | OpenAIRE |
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