Evaluation of the SUN Classification Criteria for Uveitides in an Academic Uveitis Practice

Mudie, Lucy I.; Reddy, Amit K.; Patnaik, Jennifer L; Palestine, Alan G.

doi:10.1016/j.ajo.2022.04.007

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…Of the 1143 patients seen by the practice between 1 January 2013 and 31 December 2020, 522 were diseases for which disease‐specific criteria have been developed and published by the SUN Working Group. Among these 522 patients, there was agreement between the clinician caring for the patient and the SUN classification in 94.3%, 64 suggesting clinical utility for the SUN classification criteria. Furthermore, among the cases not included in the comparative analysis, 508 had a diagnosis included in the SUN classification schema (Table 1), namely an undifferentiated uveitis 64 .…”

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 96%

“…Among these 522 patients, there was agreement between the clinician caring for the patient and the SUN classification in 94.3%, 64 suggesting clinical utility for the SUN classification criteria. Furthermore, among the cases not included in the comparative analysis, 508 had a diagnosis included in the SUN classification schema (Table 1), namely an undifferentiated uveitis 64 . The SUN Working Group has recommended that uveitis cases not meeting criteria for a specific disease be classified as undifferentiated with the course, laterality, and anatomic class (e.g., undifferentiated bilateral chronic anterior uveitis) or anatomic class with posterior involvement details (e.g., undifferentiated bilateral panuveitis with choroiditis or undifferentiated bilateral panuveitis with retinal vasculitis) 2 .…”

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 96%

“…Furthermore, among the cases not included in the comparative analysis, 508 had a diagnosis included in the SUN classification schema (Table 1), namely an undifferentiated uveitis. 64 The SUN Working Group has recommended that uveitis cases not meeting criteria for a specific disease be classified as undifferentiated with the course, laterality, and anatomic class (e.g., undifferentiated bilateral chronic anterior uveitis) or anatomic class with posterior involvement details (e.g., undifferentiated bilateral panuveitis with choroiditis or undifferentiated bilateral panuveitis with retinal vasculitis). 2 Classifying these cases as undifferentiated uveitides, would result in 97% agreement on the classification of these cases, again suggesting clinical relevance for the SUN classification criteria.…”

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 99%

“…Although the SUN classification criteria were designed as classification criteria for research use, they also may provide guidance for clinicians in clinical care. In 2022, Mudie et al 64 analysed the concordance of clinical diagnoses with SUN diagnoses among the cases seen at a large uveitis practice. Of the 1143 patients seen by the practice between 1 January 2013 and 31 December 2020, 522 were diseases for which disease‐specific criteria have been developed and published by the SUN Working Group.…”

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 99%

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The standardisation of uveitis nomenclature (SUN) project

Jabs

McCluskey

Palestine

et al. 2022

Clinical Exper Ophthalmology

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The uveitides are a collection of over 30 diseases characterised by intraocular inflammation. Previous work demonstrated that the agreement among uveitis experts on diagnosis was modest at best with some pairs of experts having chance alone agreement on selected diseases. The Standardisation of Uveitis Nomenclature (SUN) is a17‐year collaboration among experts in uveitis, ocular image grading, informatics, and machine learning to improve clinical and translational uveitis research. The SUN “Developing Classification Criteria for the Uveitides” project used a rigorous, multi‐phase approach to develop classification criteria for 25 of the most common uveitic diseases. The project's phases were: (1) informatics; (2) case collection; (3) case selection; (4) machine learning; and (5) consensus review and publication. The results were classification criteria with a high degree of accuracy (93.3%–99.3% depending on anatomic class of the uveitis), the goal of which is to form the basis for future uveitis research.

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Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 96%

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 96%

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 99%

Section: Impact and Future Expectations For The Sun Classification Cr...mentioning

confidence: 99%

See 2 more Smart Citations

The standardisation of uveitis nomenclature (SUN) project

Jabs

McCluskey

Palestine

et al. 2022

Clinical Exper Ophthalmology

Self Cite

View full text Add to dashboard Cite

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“…Limitations include the lack of an external validation set, and in terms of clinical applicability, about half of the patients had a disease for which no SUN classification criteria existed [38]. Demographic data and bias assessment were not available in those papers, along with applied models and data that were not publicly available.…”

Section: Classificationmentioning

confidence: 99%

Diagnosis, Classification, and Assessment of the Underlying Etiology of Uveitis by Artificial Intelligence: A Systematic Review

Jacquot,

Sève,

Jackson

et al. 2023

JCM

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Recent years have seen the emergence and application of artificial intelligence (AI) in diagnostic decision support systems. There are approximately 80 etiologies that can underly uveitis, some very rare, and AI may lend itself to their detection. This synthesis of the literature selected articles that focused on the use of AI in determining the diagnosis, classification, and underlying etiology of uveitis. The AI-based systems demonstrated relatively good performance, with a classification accuracy of 93–99% and a sensitivity of at least 80% for identifying the two most probable etiologies underlying uveitis. However, there were limitations to the evidence. Firstly, most data were collected retrospectively with missing data. Secondly, ophthalmic, demographic, clinical, and ancillary tests were not reliably integrated into the algorithms’ dataset. Thirdly, patient numbers were small, which is problematic when aiming to discriminate rare and complex diagnoses. In conclusion, the data indicate that AI has potential as a diagnostic decision support system, but clinical applicability is not yet established. Future studies and technologies need to incorporate more comprehensive clinical data and larger patient populations. In time, these should improve AI-based diagnostic tools and help clinicians diagnose, classify, and manage patients with uveitis.

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