Although the association between persistent hypertension and the compromise of both micro- and macro-circulatory functions is well recognized, a significant gap in quantitative investigations exploring the interplay between microvascular and macrovascular injuries still exists. In this study, the authors looked into the relationship between brachial-ankle pulse wave velocity (baPWV) and hypertensive retinopathy in treated hypertensive adults. The authors conducted a cross-sectional study of treated hypertensive patients with the last follow-up data from the China Stoke Primary Prevention Trial (CSPPT) in 2013. With the use of PWV/ABI instruments, baPWV was automatically measured. The Keith-Wagener-Barker classification was used to determine the diagnosis of hypertensive retinopathy. The odds ratio (OR) and 95% confidence interval (CI) for the connection between baPWV and hypertensive retinopathy were determined using multivariable logistic regression models. The OR curves were created using a multivariable-adjusted restricted cubic spline model to investigate any potential non-linear dose-response relationships between baPWV and hypertensive retinopathy. A total of 8514 (75.5%) of 11,279 participants were diagnosed with hypertensive retinopathy. The prevalence of hypertensive retinopathy increased from the bottom quartile of baPWV to the top quartile: quartile 1: 70.7%, quartile 2: 76.1%, quartile 3: 76.7%, quartile 4: 78.4%. After adjusting for potential confounders, baPWV was positively associated with hypertensive retinopathy (OR = 1.05, 95% CI, 1.03-1.07, p < .001). Compared to those in the lowest baPWV quartile, those in the highest baPWV quartile had an odds ratio for hypertensive retinopathy of 1.61 (OR = 1.61, 95% CI: 1.37-1.89, p < .001). Two-piece-wise logistic regression model demonstrated a nonlinear relationship between baPWV and hypertensive retinopathy with an inflection point of 17.1 m/s above which the effect was saturated .

OBJECTIVE: We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images.
METHODS: Cross sectional study.
METHODS: Participants of Beijing Eye Study 2011 and patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center.
METHODS: We trained and validated a deep learning algorithm to assess cognitive impairment using retrospectively collected data from the Beijing Eye Study 2011. Cognitive impairment was defined as a Mini-Mental State Examination score < 24. Based on fundus photographs and OCT images, we developed 5 models based on the following sets of images: macula-centered fundus photographs, optic disc-centered fundus photographs, fundus photographs of both fields, OCT images, and fundus photographs of both fields with OCT (multimodal). The performance of the models was evaluated and compared in an external validation data set, which was collected from patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center.
METHODS: Area under the curve (AUC).
RESULTS: A total of 9424 retinal photographs and 4712 OCT images were used to develop the model. The external validation sets from each center included 1180 fundus photographs and 590 OCT images. Model comparison revealed that the multimodal performed best, achieving an AUC of 0.820 in the internal validation set, 0.786 in external validation set 1, and 0.784 in external validation set 2. We evaluated the performance of the multi-model in different sexes and different age groups; there were no significant differences. The heatmap analysis showed that signals around the optic disc in fundus photographs and the retina and choroid around the macular and optic disc regions in OCT images were used by the multimodal to identify participants with cognitive impairment.
CONCLUSIONS: Fundus photographs and OCT can provide valuable information on cognitive function. Multimodal models provide richer information compared with single-mode models. Deep learning algorithms based on multimodal retinal images may be capable of screening cognitive impairment. This technique has potential value for broader implementation in community-based screening or clinic settings.
BACKGROUND: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Chorioretinal atrophy with pigmentation along the retinal veins was observed in the right fundus of a 49-year-old patient. Extensive retinitis pigmentosa (RP) was observed in the left eye. Dynamic quantitative visual field testing revealed a scotoma in the right eye that corresponded to the area of ​​retinochoroidal atrophy and afferent visual field constriction was observed on the left eye. An electroretinogram test revealed that the right eye showed attenuated type and the left eye showed negative type. Thus, the conditions of his right eye and left eye were diagnosed as pigmented paravenous retinochoroidal atrophy (PPRCA) and RP, respectively. Thus, there may be a higher proportion of PPRCA patients with unilateral RP than expected.

Optic disc swelling is a manifestation of a broad range of processes affecting the optic nerve head and/or the anterior segment of the optic nerve. Accurately diagnosing optic disc oedema, grading its severity, and recognising its cause, is crucial in order to treat patients in a timely manner and limit vision loss. Some ocular fundus features, in light of a patient\'s history and visual symptoms, may suggest a specific mechanism or aetiology of the visible disc oedema, but current criteria can at most enable an educated guess as to the most likely cause. In many cases only the clinical evolution and ancillary testing can inform the exact diagnosis. The development of ocular fundus imaging, including colour fundus photography, fluorescein angiography, optical coherence tomography, and multimodal imaging, has provided assistance in quantifying swelling, distinguishing true optic disc oedema from pseudo-optic disc oedema, and differentiating among the numerous causes of acute optic disc oedema. However, the diagnosis of disc oedema is often delayed or not made in busy emergency departments and outpatient neurology clinics. Indeed, most non-eye care providers are not able to accurately perform ocular fundus examination, increasing the risk of diagnostic errors in acute neurological settings. The implementation of non-mydriatic fundus photography and artificial intelligence technology in the diagnostic process addresses these important gaps in clinical practice.

OBJECTIVE: To describe the alterations of the peripheral retina in extensive macular atrophy with pseudodrusen-like deposits (EMAP) by means of ultrawidefield fundus photography (UWFFP) and ultrawidefield fundus autofluorescence (UWF-FAF).
METHODS: Prospective, observational case series.
METHODS: Twenty-three patients affected by EMAP.
METHODS: Each patient underwent best-corrected visual acuity (BCVA) measurement, UWFFP, and UWF-FAF. The area of macular atrophy, as well as the pseudodrusen-like deposits and peripheral degeneration, were assessed using UWF images, at baseline and over the follow-up.
METHODS: The assessment of the clinical patterns of both pseudodrusen-like deposits and peripheral retinal degeneration. Secondary outcomes included assessing macular atrophy by means of UWFFP and UWF-FAF, and tracking progression over the follow-up.
RESULTS: Twenty-three patients (46 eyes) were included, of whom 14 (60%) were female. Mean age was 59.0 ± 5 years. Mean BCVA at baseline was 0.4 ± 0.4, declining at a mean rate of 0.13 ± 0.21 logarithm of the minimum angle of resolution/year. Macular atrophy at baseline was 18.8 ± 14.2 mm2 on UWF-FAF, enlarging at a rate of 0.46 ± 0.28 mm/year, after the square root transformation. Pseudodrusen-like deposits were present in all cases at baseline, and their detection decreased over the follow-up. Three main types of peripheral degeneration were identified: retinal pigment epithelium alterations, pavingstone-like changes, and pigmented chorioretinal atrophy. Peripheral degeneration progressed in 29 eyes (63.0%), at a median rate of 0.7 (interquartile range, 0.4-1.2) sectors/year.
CONCLUSIONS: Extensive macular atrophy with pseudodrusen-like deposits is a complex disease involving not only the macula, but also the midperiphery and the periphery of the retina.
BACKGROUND: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

UNASSIGNED: The aim of this study was to prospectively quantify the level of agreement among the deep learning system, non-physician graders, and general ophthalmologists with different levels of clinical experience in detecting referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy.
UNASSIGNED: Deep learning systems for diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy classification, with accuracy proven through internal and external validation, were established using 210,473 fundus photographs. Five trained non-physician graders and 47 general ophthalmologists from China were chosen randomly and included in the analysis. A test set of 300 fundus photographs were randomly identified from an independent dataset of 42,388 gradable images. The grading outcomes of five retinal and five glaucoma specialists were used as the reference standard that was considered achieved when ≥50% of gradings were consistent among the included specialists. The area under receiver operator characteristic curve of different groups in relation to the reference standard was used to compare agreement for referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy.
UNASSIGNED: The test set included 45 images (15.0%) with referable diabetic retinopathy, 46 (15.3%) with age-related macular degeneration, 46 (15.3%) with glaucomatous optic neuropathy, and 163 (55.4%) without these diseases. The area under receiver operator characteristic curve for non-physician graders, ophthalmologists with 3-5 years of clinical practice, ophthalmologists with 5-10 years of clinical practice, ophthalmologists with >10 years of clinical practice, and the deep learning system for referable diabetic retinopathy were 0.984, 0.964, 0.965, 0.954, and 0.990 (p = 0.415), respectively. The results for referable age-related macular degeneration were 0.912, 0.933, 0.946, 0.958, and 0.945, respectively, (p = 0.145), and 0.675, 0.862, 0.894, 0.976, and 0.994 for referable glaucomatous optic neuropathy, respectively (p < 0.001).
UNASSIGNED: The findings of this study suggest that the accuracy of this deep learning system is comparable to that of trained non-physician graders and general ophthalmologists for referable diabetic retinopathy and age-related macular degeneration, but the deep learning system performance is better than that of trained non-physician graders for the detection of referable glaucomatous optic neuropathy.

This study describes the development of a convolutional neural network (CNN) for automated assessment of optic disc photograph quality. Using a code-free deep learning platform, a total of 2377 optic disc photographs were used to develop a deep CNN capable of determining optic disc photograph quality. Of these, 1002 were good-quality images, 609 were acceptable-quality, and 766 were poor-quality images. The dataset was split 80/10/10 into training, validation, and test sets and balanced for quality. A ternary classification model (good, acceptable, and poor quality) and a binary model (usable, unusable) were developed. In the ternary classification system, the model had an overall accuracy of 91% and an AUC of 0.98. The model had higher predictive accuracy for images of good (93%) and poor quality (96%) than for images of acceptable quality (91%). The binary model performed with an overall accuracy of 98% and an AUC of 0.99. When validated on 292 images not included in the original training/validation/test dataset, the model\'s accuracy was 85% on the three-class classification task and 97% on the binary classification task. The proposed system for automated image-quality assessment for optic disc photographs achieves high accuracy in both ternary and binary classification systems, and highlights the success achievable with a code-free platform. There is wide clinical and research potential for such a model, with potential applications ranging from integration into fundus camera software to provide immediate feedback to ophthalmic photographers, to prescreening large databases before their use in research.

UNASSIGNED: The curation of images using human resources is time intensive but an essential step for developing artificial intelligence (AI) algorithms. Our goal was to develop and implement an AI algorithm for image curation in a high-volume setting. We also explored AI tools that will assist in deploying a tiered approach, in which the AI model labels images and flags potential mislabels for human review.
UNASSIGNED: Implementation of an AI algorithm.
UNASSIGNED: Seven-field stereoscopic images from multiple clinical trials.
UNASSIGNED: The 7-field stereoscopic image protocol includes 7 pairs of images from various parts of the central retina along with images of the anterior part of the eye. All images were labeled for field number by reading center graders. The model output included classification of the retinal images into 8 field numbers. Probability scores (0-1) were generated to identify misclassified images, with 1 indicating a high probability of a correct label.
UNASSIGNED: Agreement of AI prediction with grader classification of field number and the use of probability scores to identify mislabeled images.
UNASSIGNED: The AI model was trained and validated on 17 529 images and tested on 3004 images. The pooled agreement of field numbers between grader classification and the AI model was 88.3% (kappa, 0.87). The pooled mean probability score was 0.97 (standard deviation [SD], 0.08) for images for which the graders agreed with the AI-generated labels and 0.77 (SD, 0.19) for images for which the graders disagreed with the AI-generated labels (P < 0.0001). Using receiver operating characteristic curves, a probability score of 0.99 was identified as a cutoff for distinguishing mislabeled images. A tiered workflow using a probability score of < 0.99 as a cutoff would include 27.6% of the 3004 images for human review and reduce the error rate from 11.7% to 1.5%.
UNASSIGNED: The implementation of AI algorithms requires measures in addition to model validation. Tools to flag potential errors in the labels generated by AI models will reduce inaccuracies, increase trust in the system, and provide data for continuous model development.

Glaucoma is the leading cause of irreversible blindness, and the early detection and timely treatment are essential for glaucoma management. However, due to the interindividual variability in the characteristics of glaucoma onset, a single feature is not yet sufficient for monitoring glaucoma progression in isolation. There is an urgent need to develop more comprehensive diagnostic methods with higher accuracy. In this study, we proposed a multi- feature deep learning (MFDL) system based on intraocular pressure (IOP), color fundus photograph (CFP) and visual field (VF) to classify the glaucoma into four severity levels. We designed a three-phase framework for glaucoma severity diagnosis from coarse to fine, which contains screening, detection and classification. We trained it on 6,131 samples from 3,324 patients and tested it on independent 240 samples from 185 patients. Our results show that MFDL achieved a higher accuracy of 0.842 (95 % CI, 0.795-0.888) than the direct four classification deep learning (DFC-DL, accuracy of 0.513 [0.449-0.576]), CFP-based single-feature deep learning (CFP-DL, accuracy of 0.483 [0.420-0.547]) and VF-based single-feature deep learning (VF-DL, accuracy of 0.725 [0.668-0.782]). Its performance was statistically significantly superior to that of 8 juniors. It also outperformed 3 seniors and 1 expert, and was comparable with 2 glaucoma experts (0.842 vs 0.854, p = 0.663; 0.842 vs 0.858, p = 0.580). With the assistance of MFDL, junior ophthalmologists achieved statistically significantly higher accuracy performance, with the increased accuracy ranged from 7.50 % to 17.9 %, and that of seniors and experts were 6.30 % to 7.50 % and 5.40 % to 7.50 %. The mean diagnosis time per patient of MFDL was 5.96 s. The proposed model can potentially assist ophthalmologists in efficient and accurate glaucoma diagnosis that could aid the clinical management of glaucoma.

UNASSIGNED: To assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields.
UNASSIGNED: Algorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study.
UNASSIGNED: Fundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models.
UNASSIGNED: Accuracy and area under the receiver-operating characteristic curve (AUC).
UNASSIGNED: Fundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model.
UNASSIGNED: The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89-0.92), 0.89 (0.88-0.91), and 0.94 (0.92-0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92-0.95), 0.98 (0.98-0.99), and 0.98 (0.98-0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively.
UNASSIGNED: Probabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making.