Classification Criteria for Serpiginous Choroiditis

To determine classification criteria for serpiginous choroiditis. Machine learning of cases with serpiginous choroiditis and 8 other posterior uveitides. Cases of posterior uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achievi...

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Bibliographic Details
Published inAmerican journal of ophthalmology Vol. 228; pp. 126 - 133
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.08.2021
Elsevier Limited
Subjects
Adult
Atrophy
Choroid - diagnostic imaging
Classification
Disease
Female
Fluorescein Angiography - methods
Fundus Oculi
Humans
Informatics
Machine Learning
Male
Medical imaging
Middle Aged
Tuberculosis
White Dot Syndromes - classification
White Dot Syndromes - diagnosis
Working groups
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Summary:To determine classification criteria for serpiginous choroiditis. Machine learning of cases with serpiginous choroiditis and 8 other posterior uveitides. 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 uveitides / panuveitides. The resulting criteria were evaluated on the validation set. One thousand sixty-eight cases of posterior uveitides, including 122 cases of serpiginous choroiditis, were evaluated by machine learning. Key criteria for serpiginous choroiditis included (1) choroiditis with an ameboid or serpentine shape; (2) characteristic imaging on fluorescein angiography or fundus autofluorescence; (3) absent to mild anterior chamber and vitreous inflammation; and (4) the exclusion of tuberculosis. 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 serpiginous choroiditis were 0% in both the training set and the validation set. The criteria for serpiginous choroiditis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research.
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Inquiries to Douglas A. Jabs, Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 20215, USA.
Members of the SUN Working Group are listed online at AJO.com.
CREDIT ROLES
Writing Committee: Douglas A. Jabs1,2, Antoine P. Brezin3, Ralph D. Levinson4, Neal Oden5, Alan G. Palestine6, Narsing A. Rao7, Jennifer E. Thorne1,2, Brett E. Trusko8, Albert Vitale9, and Susan E. Wittenberg10
Douglas A. Jabs, MD, MBA: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing – Review and editing, Visualization, Supervision, Project administration, Funding acquisition. Antoine P. Brezin, MD: Investigation, Writing – Review and editing. Ralph D. Levinson, MD: Investigation, Writing – Review and editing. Neal Oden, PhD: Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – Review and editing. Alan G. Palestine, MD: Investigation, Writing – Original draft, Writing – Review and editing. Narsing A. Rao, MD: Investigation, Writing – Review and editing. Jennifer E. Thorne, MD, PhD: Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – Review and editing. Brett E. Trusko, PhD, MBA: Methodology, Software, Resources, Data curation, Investigation, Writing – Review and editing. Albert Vitale, MD: Investigation, Writing – Review and editing. Susan E. Wittenberg, MD: Investigation, Writing – Review and editing.
Writing Committee Affiliations: From the 1 Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA; the 2 Wilmer Eye Institute, Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; the 3 Department of Ophthalmology, University of Paris V – Hôpital Cochin, Paris, France; the 4 Jules Stein Eye Institute, Department of Ophthalmology, University of California, Los Angeles Geffen School of Medicine, Los Angeles, California, USA; the 5 The Emmes Company, LLC, Rockville, Maryland, USA; the 6 Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado, USA; the 7 USC Roski Eye Institute, Department of Ophthalmology, University of Southern California School of Medicine, Los Angeles, California, USA; the 8 Department of Medicine, Texas A&M University, College Station, Texas, USA; the 9 Department of Ophthalmology, the University of Utah School of Medicine, Salt Lake City, Utah, USA; and the 10 Houston Eye Associates, Houston, Texas, USA.
ISSN:0002-9394
1879-1891
DOI:10.1016/j.ajo.2021.03.038