Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT.

PURPOSE: To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retinal nerve fiber layer (RNFL) thickness. DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 14 034 SD-OCT scans from 816 eyes from 462 individuals. METHODS: A DL convolutional neural network was trained to assess SD-OCT RNFL thickness measurements of 2 visits (a baseline and a follow-up visit) along with time between visits to predict the probability of glaucoma progression. The ground truth was defined by consensus from subjective grading by glaucoma specialists. Diagnostic performance was summarized by the area under the receiver operator characteristic curve (AUC), sensitivity, and specificity, and was compared with conventional trend-based analyses of change. Interval likelihood ratios were calculated to determine the impact of DL model results in changing the post-test probability of progression. MAIN OUTCOME MEASURES: The AUC, sensitivity, and specificity of the DL model. RESULTS: The DL model had an AUC of 0.938 (95% confidence interval [CI], 0.921-0.955), with sensitivity of 87.3% (95% CI, 83.6%-91.6%) and specificity of 86.4% (95% CI, 79.9%-89.6%). When matched for the same specificity, the DL model significantly outperformed trend-based analyses. Likelihood ratios for the DL model were associated with large changes in the probability of progression in the vast majority of SD-OCT tests. CONCLUSIONS: A DL model was able to assess the probability of glaucomatous structural progression from SD-OCT RNFL thickness measurements. The model agreed well with expert judgments and outperformed conventional trend-based analyses of change, while also providing indication of the likely locations of change. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Predicting Age From Optical Coherence Tomography Scans With Deep Learning.

Purpose: To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions. Methods: Deep learning (DL) convolutional neural networks were developed to predict chronological age in healthy subjects using peripapillary SD-OCT B-scan images. Models were built using the whole B-scan, as well as using specific regions through image ablation. Cross-validation was used for training and testing the model. Mean absolute error (MAE) and correlations between predicted and observed age were used to evaluate model performance. Results: A total of 7271 images from 542 eyes of 278 healthy subjects were included. DL predictions of age using the whole B-scan were strongly correlated with chronological age (MAE = 5.82 years; r = 0.860, P < 0.001). The model also accurately discriminated between the lowest and highest tertiles of age, with an area under the receiver operating characteristic curve of 0.962. In general, class activation maps tended to show a diffuse pattern of activation throughout the scan image. For specific structures of the B-scan, the layers with the strongest correlations with chronological age were the choroid and vitreous (both r = 0.736), whereas retinal nerve fiber layer had the lowest correlation (r = 0.492). Conclusions: A DL algorithm was able to accurately predict age from whole peripapillary SD-OCT B-scans. Translational Relevance: DL models applied to SD-OCT scans suggest that aging appears to affect several layers in the posterior eye segment.

An objective structural and functional reference standard in glaucoma.

The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In the present study, we propose an objective definition of glaucomatous optic neuropathy (GON) using clearly defined parameters from optical coherence tomography and standard automated perimetry. We then use the proposed objective definition as reference standard to develop a DL algorithm to detect GON on fundus photos. A DL algorithm was trained to detect GON on fundus photos, using the proposed objective definition as reference standard. The performance was evaluated on an independent test sample with sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and likelihood ratios (LR). The test sample had 2118 fundus photos from 585 eyes of 405 individuals. The AUC to discriminate between GON and normal was 0.92 with sensitivity of 77% at 95% specificity. LRs indicated that the DL algorithm provided large changes in the post-test probability of disease for the majority of eyes. A DL algorithm to evaluate fundus photos had high performance to discriminate GON from normal. The newly proposed objective definition of GON used as reference standard may increase the comparability of diagnostic studies of glaucoma across devices and populations.

Impact of Intraocular Pressure Control on Rates of Retinal Nerve Fiber Layer Loss in a Large Clinical Population.

PURPOSE: To investigate the impact of intraocular pressure (IOP) control on rates of change of spectral-domain OCT (SD-OCT) retinal nerve fiber layer (RNFL) thickness in a large clinical population. DESIGN: Retrospective cohort study. PARTICIPANTS: A total of 85 835 IOP measurements and 60 223 SD-OCT tests from 14 790 eyes of 7844 patients. METHODS: Data were extracted from the Duke Glaucoma Registry, a large database of electronic medical records of patients with glaucoma and suspected disease followed over time at the Duke Eye Center and satellite clinics. All records from patients with a minimum of 6 months of follow-up and at least 2 good-quality SD-OCT scans and 2 clinical visits with Goldmann applanation tonometry were included. Eyes were categorized according to the frequency of visits with IOP below cutoffs of 21 mmHg, 18 mmHg, and 15 mmHg over time. Rates of change for global RNFL thickness were obtained using linear mixed models and classified as slow if change was slower than -1.0 µm/year; moderate if between -1.0 and -2.0 µm/year; and fast if faster than -2.0 µm/year. Multivariable models were adjusted for age, gender, race, diagnosis, central corneal thickness, follow-up time, and baseline disease severity. MAIN OUTCOME MEASURES: Rates of change in SD-OCT RNFL thickness according to levels of IOP control. RESULTS: Eyes had a mean follow-up of 3.5±1.9 years. Average rate of change in RNFL thickness was -0.68±0.59 µm/year. Each 1 mmHg higher mean IOP was associated with 0.05 µm/year faster RNFL loss (P < 0.001) after adjustment for potentially confounding variables. For eyes that had fast progression, 41% of them had IOP <21 mmHg in all visits during follow-up, whereas 20% of them had all visits with IOP <18 mmHg, but only 9% of them had all visits with IOP <15 mmHg. CONCLUSIONS: Intraocular pressure was significantly associated with rates of progressive RNFL loss in a large clinical population. Eyes with stricter IOP control over follow-up visits had a smaller chance of exhibiting fast deterioration. Our findings may assist clinicians in establishing target pressures in clinical practice.

Primary Trabeculectomy Outcomes by Glaucoma Fellows in a Tertiary Hospital in Brazil.

PURPOSE: To examine outcomes of trabeculectomy with mitomycin C for uncontrolled glaucoma when performed by glaucoma trainee surgeons. MATERIALS AND METHODS: Retrospective case series of patients who underwent trabeculectomy with mitomycin C. Primary outcome was to assess the rate of failure, which was defined as intraocular pressure in 2 consecutive visits >18 or <5 mm Hg or intraocular pressure reduction <30% from baseline, additional glaucoma surgery, or loss of light perception. The secondary outcome was to investigate risk factors for failure and surgical complications. RESULTS: One hundred forty-three eyes from 126 glaucoma patients underwent primary trabeculectomy between 2013 and 2014 at University of Campinas. Mean follow-up time was 1.47±0.96 years. Kaplan-Meier analysis showed cumulative survival rates of 70.1%, 62.5%, and 57.8%, after 1, 2, and 3 years, respectively. Multivariate analysis with generalized estimating equations revealed that neovascular glaucoma [odd ratios (OR): 5.05, P=0.043], suture lysis (OR: 8.89, P=0.011), and early bleb leak (OR: 4.63, P=0.011) were risk factors for failure. CONCLUSIONS: A success rate of approximately 60% was obtained 3 years after primary trabeculectomy performed by trainees. Patients with neovascular glaucoma, who underwent suture lysis and who presented an early bleb leak had an increased risk for failure during follow-up.