Prediction of visual field progression with serial optic disc photographs using deep learning
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Le résumé fourni par la source
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up. METHODS: 3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24-2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy. RESULTS: The average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and -3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5-11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812-0.913) and 80.0% (73.9%-84.6%). When only fast-progressing eyes were considered (MD rate < -1.0 dB/year), AUC increased to 0.926 (0.857-0.994). CONCLUSIONS: A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Prediction of visual field progression with serial optic disc photographs using deep learning
- Date Crossref
- 13/10/2023
- Éditeur
- BMJ
- 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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Doheny Eye Institute pays non établi dans la noticeStructure de recherche
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Pepperdine University Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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University of California Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Sherman Oaks Hospital pays non établi dans la noticeÉtablissement de santé
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Jules Stein Eye Institute Department of Ophthalmology pays non établi dans la noticeStructure de recherche
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Jules Stien Eye Institute Department of Ophthalmology pays non établi dans la noticeStructure de recherche
Doheny Eye Institute, Department of Computer Science — Pepperdine University et Department of Computer Science — University of California, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.