Aim. To asses both choroidal thickness differences among Alzheimer's type dementia (ATD) patients, mild cognitive impairment (MCI) patients, and healthy control (C) subjects and choroidal thickness relationships with cognitive performance. Methods. A total of 246 eyes of 123 people (41 ATD, 38 MCI, and 44 healthy C subjects) were included in this study. Complete ophthalmological and neurological examination was performed in all subjects. Choroidal thicknesses (CT) were measured at seven locations: the fovea, 500-1500-3000 μm temporal and 500-1500-3000 μm nasal to the fovea by enhanced depth imaging optical coherence tomography (EDI-OCT). Detailed neurological examination including mini mental state examination (MMSE) test which evaluates the cognitive function was applied to all participants. Results. The ages and genders of all participants were similar in all groups. Compared with healthy C subjects, the CT measurements at all regions were significantly thinner both in patients with ATD and in patients with MCI than in healthy C subjects (p < 0.05). The MMSE scores were significantly different among ATD patients, MCI patients, and healthy C subjects. They were 19.3 ± 1.8, 24.8 ± 0.9, and 27.6 ± 1.2 in ATD, MCI, and healthy controls, respectively (p < 0.001). There were also significant correlation between MMSE score and choroidal thickness at each location (p < 0.05). Conclusions. CT was reduced in ATD patients and MCI patients. Since vascular structures were affected in ATD patients and MCI patients, they had thin CT. Besides CT was correlated with degree of cognitive impairment. Therefore CT may be a new biomarker in diagnosis and follow-up of MCI and ATD patients.

As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed. Impact statement OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. Quantitative analysis of OCTA is essential to standardize objective interpretations of clinical outcome. This review summarizes technical rationales and clinical applications of quantitative OCTA features.

To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy. Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform. Results: With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment. Conclusions: With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients. Translational Relevance: Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.