Development and Validation of a Bayesian Network for Supporting the Etiological Diagnosis of Uveitis
The etiological diagnosis of uveitis is complex. We aimed to implement and validate a Bayesian belief network algorithm for the differential diagnosis of the most relevant causes of uveitis. The training dataset (n = 897) and the test dataset (n = 154) were composed of all incident cases of uveitis admitted to two internal medicine departments, in two independent French centers (Lyon, 2003-2016 and Dijon, 2015-2017). The etiologies of uveitis were classified into eight groups. The algorithm was based on simple epidemiological characteristics (age, gender, and ethnicity) and anatomoclinical features of uveitis. The cross-validated estimate obtained in the training dataset concluded that the etiology of uveitis determined by the experts corresponded to one of the two most probable diagnoses in at least 77% of the cases. In the test dataset, this probability reached at least 83%. For the training and test datasets, when the most likely diagnosis was considered, the highest sensitivity was obtained for spondyloarthritis and HLA-B27-related uveitis (76% and 63%, respectively). The respective specificities were 93% and 54%. This algorithm could help junior and general ophthalmologists in the differential diagnosis of uveitis. It could guide the diagnostic work-up and help in the selection of further diagnostic investigations.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Journal of clinical medicine - 10(2021), 15 vom: 30. Juli |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jamilloux, Yvan [VerfasserIn] |
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| Links: |
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| Themen: |
Algorithm |
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| Anmerkungen: |
Date Revised 20.09.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/jcm10153398 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a The etiological diagnosis of uveitis is complex. We aimed to implement and validate a Bayesian belief network algorithm for the differential diagnosis of the most relevant causes of uveitis. The training dataset (n = 897) and the test dataset (n = 154) were composed of all incident cases of uveitis admitted to two internal medicine departments, in two independent French centers (Lyon, 2003-2016 and Dijon, 2015-2017). The etiologies of uveitis were classified into eight groups. The algorithm was based on simple epidemiological characteristics (age, gender, and ethnicity) and anatomoclinical features of uveitis. The cross-validated estimate obtained in the training dataset concluded that the etiology of uveitis determined by the experts corresponded to one of the two most probable diagnoses in at least 77% of the cases. In the test dataset, this probability reached at least 83%. For the training and test datasets, when the most likely diagnosis was considered, the highest sensitivity was obtained for spondyloarthritis and HLA-B27-related uveitis (76% and 63%, respectively). The respective specificities were 93% and 54%. This algorithm could help junior and general ophthalmologists in the differential diagnosis of uveitis. It could guide the diagnostic work-up and help in the selection of further diagnostic investigations | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a algorithm | |
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| 700 | 1 | |a Romain-Scelle, Nicolas |e verfasserin |4 aut | |
| 700 | 1 | |a Rabilloud, Muriel |e verfasserin |4 aut | |
| 700 | 1 | |a Morel, Coralie |e verfasserin |4 aut | |
| 700 | 1 | |a Kodjikian, Laurent |e verfasserin |4 aut | |
| 700 | 1 | |a Maucort-Boulch, Delphine |e verfasserin |4 aut | |
| 700 | 1 | |a Bielefeld, Philip |e verfasserin |4 aut | |
| 700 | 1 | |a Sève, Pascal |e verfasserin |4 aut | |
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