Deep-Learning–Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Purpose: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies. Design: Cross sectional study. Participants: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma. Methods: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis. Main Outcome Measures: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework. Results: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02. Conclusions: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references

Ambient particulate matter associates with asthma in high altitude region: A population-based study

Background: Exposure to particulate matter (PM) has been a major public health threat, but the potentially differential effects on asthma of PM remain largely unknown in high altitude settings. We evaluated the effects of ambient PM on asthma in high altitude settings. Methods: The study recruited a representative sample from high altitude settings using a multistage stratified sampling procedure. Asthma was defined by a self-reported history of diagnosis by a physician or by wheezing symptoms in the preceding 12 months. The annual mean PM2.5 and PM10 concentrations were calculated for each grid cell at 1-km spatial resolution based on the geographical coordinates. Results: We analyzed data for participants (mean age 39.1 years, 51.4% female) and 183 (3.7%, 95% confidence interval (CI): 3.2–4.2) of the participants had asthma. Prevalence was higher in women (4.3%, 95% CI 3.5–5.1) than in men (3.1%, 2.4–3.8) and increasing with higher concentration of PM exposures. For an interquartile range (IQR) difference (8.77 μg/m3) in PM2.5 exposure, the adjusted odds ratio (OR) was 1.64 (95% CI 1.46–1.83, P < 0.001) for risk of asthma. For PM10, there was evidence for an association with risk of asthma (OR 2.34, 95% CI: 1.75–3.15, P < 0.001 per IQR of 43.26 μg/m3). Further analyses showed that household mold or damp exposure may aggravate PM exposure associated risks of asthma. Conclusions: This study identified that PM exposure could be a dominate environmental risk factor for asthma but largely unconsidered in the high-altitude areas. The association between PM exposure and asthma should be of interest for planners of national policies and encourage programs for prevention of asthma in residents living at high altitudes