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 posterior uveitides. 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 seemed to perform sufficiently well for use in clinical and translational research.
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 posterior uveitides. 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 seemed to perform sufficiently well for use in clinical and translational research.
Authors: Douglas A Jabs; Andrew Dick; John T Doucette; Amod Gupta; Susan Lightman; Peter McCluskey; Annabelle A Okada; Alan G Palestine; James T Rosenbaum; Sophia M Saleem; Jennifer Thorne; Brett Trusko Journal: Am J Ophthalmol Date: 2017-11-06 Impact factor: 5.258
Authors: Nakhleh E Abu-Yaghi; Stella P Hartono; David O Hodge; Jose S Pulido; Sophie J Bakri Journal: Ocul Immunol Inflamm Date: 2011-12 Impact factor: 3.070
Authors: Claudio Furino; Zaid Shalchi; Maria Oliva Grassi; Joao N Cardoso; Pearse A Keane; Alfredo Niro; Maria Vittoria Cicinelli; Michele Reibaldi; Francesco Boscia; Giovanni Alessio; Carlos Pavesio Journal: Ophthalmic Surg Lasers Imaging Retina Date: 2019-07-01 Impact factor: 1.300