To investigate the potential of statistical and machine learning approaches to determine the diabetic status of patients from optical coherence tomography angiography (OCT-A) images. This was a retrospective cross-sectional observational study based at Manchester Royal Eye Hospital, United Kingdom. https://www.selleckchem.com/products/tyloxapol.html OCT-A scans were sequentially selected from one eye of each of 182 patients who were either not diabetic, diabetic without retinopathy, or diabetic with retinopathy requiring hospital follow-up. Eligible images were analyzed by expert purpose-built automated algorithms to calculate clinically relevant outcome measures. These were used in turn as inputs to machine learning and statistical procedures to derive algorithms to perform clinically relevant classifications of patient images into the clinical groups. Receiver operating characteristic curves for the classifiers were evaluated and predictive accuracy assessed using area under curve (AUC). For distinguishing diabetic patients from those without diabetes, the Random Forest classifier provided the highest AUC (0.8). For distinguishing diabetic patients with significant retinopathy from those with no retinopathy, the highest AUC was represented by logistic regression (0.91). The study demonstrates the potential of novel techniques using automated analysis of OCT-A scans to diagnose patients with diabetes, or when diabetic status is known, to automatically determine those that require hospital input. This work advances the concept of a rapid and noninvasive clinical screening tool using OCT-A to determine a patient's diabetic status. This work advances the concept of a rapid and noninvasive clinical screening tool using OCT-A to determine a patient's diabetic status. Continuous monitoring of elevated intraocular pressure and timely drug delivery for successful treatment of glaucoma are necessary to reduce intraocular pressure (IOP), which shows wide variations across the circadian pattern and in response to medication. This in vivo study presents a new contact lens-based method of optical IOP measurement or temperature-triggered drug elution. A contact lens with moiré patterns of concentric circles measures the changes in eyeball diameter of a rabbit glaucoma model due to changes in IOP by superimposing a camera-captured image onto the micro pattern of the contact lens with a computer-assisted virtual reference image. Drug elution from the nanoporous bicontinuous microemulsion contact lens (BME-CL) into the eye of the rabbit was triggered by a temperature-responsive nanogel drug carrier. The moiré pattern change on the contact lens was proportional to the IOP increase in the rabbit eye either ex vivo or in vivo and was also correlated with imaging-based alterations in the anterior chamber angle at a range of IOP values (3-40 mm Hg). The cumulative drug absorbed reached as high as 10.6 µg/mL aqueous humor until 7 days after wearing the BME-CL, and a 33% decrease in IOP was observed at 3 hours after drug elution. The results suggest that continuous measurement and treatment of elevated IOP are feasible using moiré pattern-inscribed and thermosensitive drug-eluting contact lenses, respectively. Pressure-sensing or thermosensitive contact lenses enable monitoring IOP or drug release triggered by body temperature for the treatment of glaucoma patients. Pressure-sensing or thermosensitive contact lenses enable monitoring IOP or drug release triggered by body temperature for the treatment of glaucoma patients. To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models. Two fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorithms. The original University of California, San Diego and University of Tokyo models performed similarly (area under the receiver operating characteristic curve = 0.96 and 0.97, respectively) for detection of glaucoma in the Matsue Red Cross Hospital imary care. High sensitivity and specificity of deep learning algorithms for moderate-to-severe glaucoma across diverse populations suggest a role for artificial intelligence in the detection of glaucoma in primary care. To compare frequently used classification systems for age-related macular degeneration (AMD) in their abilty to predict late AMD. In total, 9066 participants from the population-based Rotterdam Study were followed up for progression of AMD during a study period up to 30 years. AMD lesions were graded on color fundus photographs after confirmation on other image modalities and grouped at baseline according to six classification systems. Late AMD was defined as geographic atrophy or choroidal neovascularization. Incidence rate (IR) and cumulative incidence (CuI) of late AMD were calculated, and Kaplan-Meier plots and area under the operating characteristics curves (AUCs) were constructed. A total of 186 persons developed incident late AMD during a mean follow-up time of 8.7 years. The AREDS simplified scale showed the highest IR for late AMD at 104 cases/1000 py for ages <75 years. The Rotterdam classification showed the highest IR at 89 cases/1000 py >75 years. The 3-Continent harmonization classification provided the most stable progression. Drusen area >10% ETDRS grid (hazard ratio 30.05, 95% confidence interval [CI] 19.25-46.91) was most prognostic of progression. The highest AUC of late AMD (0.8372, 95% CI 0.8070-0.8673) was achieved when all AMD features present at baseline were included. Highest turnover rates from intermediate to late AMD were provided by the AREDS simplified scale and the Rotterdam classification. The 3-Continent harmonization classification showed the most stable progression. All features, especially drusen area, contribute to late AMD prediction. Findings will help stakeholders select appropriate classification systems for screening, deep learning algorithms, or trials. Findings will help stakeholders select appropriate classification systems for screening, deep learning algorithms, or trials.