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Using Fused Data from Perimetry and Optical Coherence Tomography to Improve the Detection of Visual Field Progression in Glaucoma.
Li-Han, Leo Yan; Eizenman, Moshe; Shi, Runjie Bill; Buys, Yvonne M; Trope, Graham E; Wong, Willy.
Afiliación
  • Li-Han LY; The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada.
  • Eizenman M; Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, ON M5T 3A9, Canada.
  • Shi RB; Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.
  • Buys YM; Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada.
  • Trope GE; Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3E2, Canada.
  • Wong W; Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, ON M5T 3A9, Canada.
Bioengineering (Basel) ; 11(3)2024 Mar 03.
Article en En | MEDLINE | ID: mdl-38534524
ABSTRACT
Perimetry and optical coherence tomography (OCT) are both used to monitor glaucoma progression. However, combining these modalities can be a challenge due to differences in data types. To overcome this, we have developed an autoencoder data fusion (AEDF) model to learn compact encoding (AE-fused data) from both perimetry and OCT. The AEDF model, optimized specifically for visual field (VF) progression detection, incorporates an encoding loss to ensure the interpretation of the AE-fused data is similar to VF data while capturing key features from OCT measurements. For model training and evaluation, our study included 2504 longitudinal VF and OCT tests from 140 glaucoma patients. VF progression was determined from linear regression slopes of longitudinal mean deviations. Progression detection with AE-fused data was compared to VF-only data (standard clinical method) as well as data from a Bayesian linear regression (BLR) model. In the initial 2-year follow-up period, AE-fused data achieved a detection F1 score of 0.60 (95% CI 0.57 to 0.62), significantly outperforming (p < 0.001) the clinical method (0.45, 95% CI 0.43 to 0.47) and the BLR model (0.48, 95% CI 0.45 to 0.51). The capacity of the AEDF model to generate clinically interpretable fused data that improves VF progression detection makes it a promising data integration tool in glaucoma management.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioengineering (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioengineering (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Suiza