Objective To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests. Methods In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer's test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI). Results Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer's test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT. Conclusion Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.

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The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients’ confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.

To evaluate see-through Augmented Reality Digital spectacles (AR DSpecs) for improving the mobility of patients with peripheral visual field (VF) losses when tested on a walking track. Design Prospective Case Series. Participants 21 patients with peripheral VF defects in both eyes, with the physical ability to walk without assistance. Methods We developed the AR DSpecs as a wearable VF aid with an augmented reality platform. Image remapping algorithms produced personalized visual augmentation in real time based on the measured binocular VF with the AR DSpecs calibration mode. We tested the device on a walking track to determine if patients could more accurately identify peripheral objects. Main outcome measures We analyzed walking track scores (number of recognized/avoided objects) and eye tracking data (six gaze parameters) to measure changes in the kinematic and eye scanning behaviors while walking, and assessed a possible placebo effect by deactivating the AR DSpecs remapping algorithms in random trials.