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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| Published in | American journal of ophthalmology Vol. 228; pp. 126 - 133 |
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| Format | Journal Article |
| Language | English |
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United States
Elsevier Inc
01.08.2021
Elsevier Limited |
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| Abstract | 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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| AbstractList | PurposeTo determine classification criteria for serpiginous choroiditis.DesignMachine learning of cases with serpiginous choroiditis and 8 other posterior uveitides.MethodsCases 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.ResultsOne 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.ConclusionsThe criteria for serpiginous choroiditis had a low misclassification rate and seemed to perform sufficiently well for use in clinical and translational research. 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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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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. |
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| Snippet | To determine classification criteria for serpiginous choroiditis.
Machine learning of cases with serpiginous choroiditis and 8 other posterior uveitides.
Cases... PurposeTo determine classification criteria for serpiginous choroiditis.DesignMachine learning of cases with serpiginous choroiditis and 8 other posterior... PURPOSETo determine classification criteria for serpiginous choroiditis. DESIGNMachine learning of cases with serpiginous choroiditis and 8 other posterior... |
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| SubjectTerms | 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 |
| Title | Classification Criteria for Serpiginous Choroiditis |
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