An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging
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Le résumé fourni par la source
Purpose To develop an objective glaucoma damage severity classification system based on optical coherence tomography (OCT)-derived retinal nerve fiber layer (RNFL) thickness measurements. Design Algorithm development for RNFL damage severity classification based on multicenter OCT data. Subjects and Participants A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models. Methods We developed an unsupervised k -means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes' minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects. Main Outcome Measures Accuracy, area under the receiver operating characteristic curve (AUC), and confusion matrix. Results The k -means clustering discovered four clusters with 1382, 1613, 1727, and 1839 samples with mean global RNFL thickness of 58.3 μ m (±8.9: Standard Deviation), 78.9 μ m (±6.7), 87.7 μ m (±8.2), and 101.5 μ m (±7.9). The Bayes' minimum error classifier identified optimal global RNFL values of >95 μ m , 86 – 95 μ m , and 70 – 85 μ m and <70 μ m for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global or at least one quadrant RNFL thickness outside of normal limits provided by the OCT instrument. Conclusions Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 μ m , 85 μ m , and 70 μ m , respectively. This RNFL loss classification system is unbiased as there was no pre-assumption or human expert intervention in the development process. Additionally, it is objective, easy-to-use, and consistent which may augment glaucoma research and day-to-day clinical practice.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging
- Date Crossref
- 01/03/2024
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Tennessee Health Science Center Department of Genetics pays non établi dans la noticeUniversité ou école supérieure
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Rotterdam Eye Hospital pays non établi dans la noticeÉtablissement de santé
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Massachusetts Eye and Ear Infirmary pays non établi dans la noticeÉtablissement de santé
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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Smith-Kettlewell Eye Research Institute pays non établi dans la noticeOrganisation à but non lucratif
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University of California Department of Ophthalmology pays non établi dans la noticeUniversité ou école supérieure
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University of Udine pays non établi dans la noticeUniversité ou école supérieure
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University of Iowa Hospitals and Clinics Department of Ophthalmology & Visual Sciences pays non établi dans la noticeÉtablissement de santé
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Rotterdam Ophthalmic Institute pays non établi dans la noticeStructure de recherche
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Schepens Eye Research Institute of Massachusetts Eye and Ear pays non établi dans la noticeStructure de recherche
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"Città di Udine" Health Center Department of Ophthalmology pays non établi dans la noticeÉtablissement de santé
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“Città di Udine” Health Center Department of Ophthalmology pays non établi dans la noticeÉtablissement de santé
Department of Genetics — University of Tennessee Health Science Center, Rotterdam Eye Hospital et Massachusetts Eye and Ear Infirmary, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.