Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression

Abstract

Glaucoma is a chronic neurodegenerative disease characterized by loss of retinal ganglion cells, resulting in distinctive changes in the optic nerve head (ONH) and retinal nerve fiber layer. Important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, a crucial step in diagnosing and monitoring glaucoma. Three dimensional (3D) spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, is now the standard of care for diagnosing and monitoring progression of numerous eye diseases. Method This paper aims to detect changes in multi-temporal 3D SD-OCT ONH images using a hierarchical fully Bayesian framework and then to differentiate between changes reflecting random variations or true changes due to glaucoma progression. To this end, we propose the use of kernel-based support vector data description (SVDD) classifier. SVDD is a well-known one-class classifier that allows us to map the data into a high-dimensional feature space where a hypersphere encloses most patterns belonging to the target class. Results The proposed glaucoma progression detection scheme using the whole 3D SD-OCT images detected glaucoma progression in a significant number of cases showing progression by conventional methods (78%), with high specificity in normal and non-progressing eyes (93% and 94% respectively). Conclusion The use of the dependency measurement in the SVDD framework increased the robustness of the proposed change-detection scheme with comparison to the classical support vector machine and SVDD methods. The validation using clinical data of the proposed approach has shown that the use of only healthy and non-progressing eyes to train the algorithm led to a high diagnostic accuracy for detecting glaucoma progression compared to other methods.

Nerve Fiber Layer Thickness and Characteristics Associated with Glaucoma in Community Living Older Adults: Prelude to a Screening Trial?

“…It has been proposed that SD-OCT measurements be incorporated in machine-based screening for glaucomatous progression. 17 Our incidence data corroborates the notion that OCT changes may presage incident glaucoma. A meta-analysis of screening for glaucoma that incorporates SD-OCT suggests that such an approach is feasible.…”

Hybrid Deep Learning on Single Wide-field Optical Coherence tomography Scans Accurately Classifies Glaucoma Suspects

“…Several studies have employed progression of patterns 13 and unsupervised Gaussian mixture-models 14,15 on VF data. OCT has not been utilized to its full extent when training model machine learning based on OCT information.…”

Structural Change Can Be Detected in Advanced-Glaucoma Eyes

“…The Bayesian-kernel detection scheme is a Bayesian-based approach that uses change in the 3D ONH volume scans to classify an eye as “nonprogressing” or “progressing.” Details of the BKDS have been described previously. 18 , 19 In brief, raw 3D SD-OCT ONH cube scans were exported to a numerical computing language (MATLAB, MathWorks). The Bayesian-kernel detection scheme was used to estimate glaucoma progression from ONH cube scans.…”

“…Recently, we proposed a new glaucoma change detection method (Bayesian-kernel detection scheme [BKDS]) that utilizes the whole three-dimensional (3D) SD-OCT ONH and does not require extensive retinal layer segmentation. 18 , 19 In early- to moderate-glaucoma eyes with progressing VF damage, while maintaining high specificity the BKDS method had higher sensitivity for detecting progression than cpRNFL. 18 , 19 This method also has the advantage that it detects 3D change in a region of interest, which can include deeper layers such as the lamina cribrosa.…”

“… 18 , 19 In early- to moderate-glaucoma eyes with progressing VF damage, while maintaining high specificity the BKDS method had higher sensitivity for detecting progression than cpRNFL. 18 , 19 This method also has the advantage that it detects 3D change in a region of interest, which can include deeper layers such as the lamina cribrosa. Reports suggest that in eyes with advanced glaucoma, the lamina cribrosa changes its shape by developing a W-shaped configuration.…”