PURPOSE: The purpose of this study was to determine classification criteria for punctate inner choroiditis (PIC). DESIGN: Machine learning of cases with PIC 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 by using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,068 cases of posterior uveitides, including 144 cases of PIC, were evaluated by machine learning. Key criteria for PIC included: 1) "punctate"-appearing choroidal spots <250 µm in diameter; 2) absent to minimal anterior chamber and vitreous inflammation; and 3) involvement of the posterior pole with or without mid-periphery. 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 PIC were 15% in the training set and 9% in the validation set. CONCLUSIONS: The criteria for PIC had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
PURPOSE: The purpose of this study was to determine classification criteria for punctate inner choroiditis (PIC). DESIGN: Machine learning of cases with PIC 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 by using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the posterior uveitides. The resulting criteria were evaluated in the validation set. RESULTS: A total of 1,068 cases of posterior uveitides, including 144 cases of PIC, were evaluated by machine learning. Key criteria for PIC included: 1) "punctate"-appearing choroidal spots <250 µm in diameter; 2) absent to minimal anterior chamber and vitreous inflammation; and 3) involvement of the posterior pole with or without mid-periphery. 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 PIC were 15% in the training set and 9% in the validation set. CONCLUSIONS: The criteria for PIC had a reasonably low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.
Authors: Rose M Gilbert; Rachael L Niederer; Michal Kramer; Lazha Sharief; Yael Sharon; Asaf Bar; Sue Lightman; Oren Tomkins-Netzer Journal: Am J Ophthalmol Date: 2020-02-04 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: Steven Yeh; Farzin Forooghian; Wai T Wong; Lisa J Faia; Catherine Cukras; Julie C Lew; Keith Wroblewski; Eric D Weichel; Catherine B Meyerle; Hatice Nida Sen; Emily Y Chew; Robert B Nussenblatt Journal: Arch Ophthalmol Date: 2010-01
Authors: Aniruddha Agarwal; Sabia Handa; Alessandro Marchese; Salvatore Parrulli; Alessandro Invernizzi; Roel J Erckens; Tos T J M Berendschot; C A B Webers; Reema Bansal; Vishali Gupta Journal: Front Med (Lausanne) Date: 2021-12-22