Optic disc drusen (ODD) are calcium-containing deposits in the optic nerve head, capable of causing visual field defects and sudden visual loss. The underlying pathophysiology remains inadequately understood and treatment options are missing. In this paper, we systematically reviewed prevalence studies of ODD in non-selected populations to provide an overview of its prevalence, conducted meta-analyses to determine modality-specific prevalence estimates and performed a forecasting study to estimate current and future global population number of individuals with ODD. We searched 11 literature databases on 25 October 2022 for prevalence studies of ODD in non-selected populations. Eight eligible studies provided data from a total of 27 463 individuals. Prevalence estimates were stratified according to diagnostic modalities: ophthalmoscopy 0.37% (95% CI: 0.10-0.95%), fundus photography 0.12% (95% CI: 0.03-0.24%), spectral domain optical coherence tomography with enhanced depth imaging 2.21% (95% CI: 1.25-3.42%) and histopathology 1.82% (95% CI: 1.32-2.38%). Using histopathology-based summary prevalence estimate, we forecast 145 million individuals with ODD currently, a number expected to increase further due to world population growth. These numbers underscore the importance of including ODD in health education and highlight the necessity of continuing research in ODD.

The aim of this study was to use deep learning based on a deep convolutional neural network (DCNN) for automated image classification of healthy optic discs (OD) and visible optic disc drusen (ODD) on fundus autofluorescence (FAF) and color fundus photography (CFP). In this study, a total of 400 FAF and CFP images of patients with ODD and healthy controls were used. A pre-trained multi-layer Deep Convolutional Neural Network (DCNN) was trained and validated independently on FAF and CFP images. Training and validation accuracy and cross-entropy were recorded. Both generated DCNN classifiers were tested with 40 FAF and CFP images (20 ODD and 20 controls). After the repetition of 1000 training cycles, the training accuracy was 100%, the validation accuracy was 92% (CFP) and 96% (FAF), respectively. The cross-entropy was 0.04 (CFP) and 0.15 (FAF). The sensitivity, specificity, and accuracy of the DCNN for classification of FAF images was 100%. For the DCNN used to identify ODD on color fundus photographs, sensitivity was 85%, specificity 100%, and accuracy 92.5%. Differentiation between healthy controls and ODD on CFP and FAF images was possible with high specificity and sensitivity using a deep learning approach.

OBJECTIVE: Measuring the optic nerve sheath diameter (ONSD) and the anteroposterior axial length of the eye in patients with optic disc drusen (ODD).
METHODS: A total of 43 healthy volunteers and 41 patients with ODD were included in the study. The ONSD and axial length were measured in the posterior position using an ultrasound device (E-Z Scan AB5500 +) probe with a 10 MHz frequency. The ONSD was measured 3 mm behind the globe wall. Receiver operating characteristic (ROC) curve analysis was performed to determine patients with ODD using ONSD. Any p-value of < 0.05 was accepted to demonstrate significance.
RESULTS: The ONSD was significantly higher (5.2 mm and 4.8 mm, p = 0.006, respectively), and the axial length was shorter (21.82 ± 2.15 mm and 23.27 ± 1.96 mm, p = 0.002, respectively) in the ODD group. The spherical equivalent was more commonly seen as hypermetropic in the ODD group (1.00 [- 0.85 to 1.75]). In the ROC analysis to determine the ONSD value in ODD diagnosis, the area under the curve was 0.6754 (95% confidence interval 0.559-0.788, p = 0.006). ONSD cutoff of 5.70 mm had a sensitivity of 0.366 and a specificity of 0.907 to diagnose ODD.
CONCLUSIONS: In this study, the ONSD was significantly higher in the ODD group. The axial length was shorter in the ODD group. This study is the first in the literature to evaluate the ONSD in patients with optic disc drusen. Further studies are needed in this regard.