Deep Learning Algorithm for the Diagnosis and Prediction of Hydroxychloroquine Retinopathy: An International, Multi-institutional Study.

Purpose: We present a deep learning algorithm-HCQuery-that detects the presence of hydroxychloroquine retinopathy and predicts its future occurrence from spectral-domain OCT (SD-OCT) images.

Design: We trained and validated a deep learning algorithm using retrospective SD-OCT images from patients taking hydroxychloroquine.

Subjects: The study involved a retrospective, nonconsecutive collection of 409 patients (171 positive for hydroxychloroquine retinopathy and 238 negative) and 8251 SD-OCT b-scans (1988 volumes) from 5 independent international clinical locations.

A scoping review of artificial intelligence as a medical device for ophthalmic image analysis in Europe, Australia and America.

This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards identifying best practice and areas for improvement. 36 AIaMDs from 28 manufacturers were identified - 97% (35/36) approved in the EU, 22% (8/36) in Australia, and 8% (3/36) in the USA. Most targeted diabetic retinopathy detection.

Targeted Interventions Lead to Quality Improvement in Year 2 of an Artificial Intelligence-Based Diabetic Retinopathy Detection Program in Northern California.

Purpose: This study evaluates the second-year outcomes of an AI-based diabetic retinopathy (DR) detection program (Stanford Teleophthalmology Autonomous Testing and Universal Screening (STATUS)) implemented in primary care and endocrinology clinics in Northern California. We focused on assessing improvements following implementation of an intervention-based framework to increase AI system gradability and patient encounters.

Methods: A retrospective analysis was conducted involving diabetic patients aged 18 years and older with no prior DR diagnosis or examination in the past year.

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices.

Background: Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices.

Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program.

Purpose: We examine the rate of and reasons for follow-up in an Artificial Intelligence (AI)-based workflow for diabetic retinopathy (DR) screening relative to two human-based workflows.

Patients And Methods: A DR screening program initiated September 2019 between one institution and its affiliated primary care and endocrinology clinics screened 2243 adult patients with type 1 or 2 diabetes without a diagnosis of DR in the previous year in the San Francisco Bay Area. For patients who screened positive for more-than-mild-DR (MTMDR), rates of follow-up were calculated under a store-and-forward human-based DR workflow ("Human Workflow"), an AI-based workflow involving IDx-DR ("AI Workflow"), and a two-step hybrid workflow ("AI-Human Hybrid Workflow").

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography.

Importance: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans.

Objective: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan.

AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy.

Objective: Detection of diabetic retinopathy (DR) outside of specialized eye care settings is an important means of access to vision-preserving health maintenance. Remote interpretation of fundus photographs acquired in a primary care or other nonophthalmic setting in a store-and-forward manner is a predominant paradigm of teleophthalmology screening programs. Artificial intelligence (AI)-based image interpretation offers an alternative means of DR detection.

Multimodal Imaging, Tele-Education, and Telemedicine in Retinopathy of Prematurity.

Retinopathy of prematurity (ROP) is a disease that affects retinal vasculature in premature infants and remains one of the leading causes of blindness in childhood worldwide. ROP screening can encounter some difficulties such as the lack of specialists and services in rural areas. The evolution of technology has helped address these issues and led to the emergence of state-of-the-art multimodal digital imaging devices such fundus cameras with its variable properties, optical coherence tomography (OCT), OCT angiography, and fluorescein angiography which has helped immensely in the process of improving ROP care and understanding the disease pathophysiology.

Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models.

While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task.

From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration.

Objective: Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD.

Purpose: To delineate the state of AI for AMD, including current data, standards, achievements, and challenges.

Posterior Uveitis Associated with Cemiplimab.

Purpose: The immune checkpoint inhibitors (ICPIs) comprise a class of oncologic immunotherapies. The most recent US Food and Drug Administration-approved ICPI is cemiplimab (Libtayo®). Cemiplimab, like the other ICPIs, blocks checkpoint receptors in order to disinhibit T-cells so that they may detect and eliminate tumor cells.

Immune Checkpoint Inhibitor-associated Uveitis: Review of Treatments and Outcomes.

Immune checkpoint inhibitors (ICPIs), novel immunotherapy agents employed in the treatment of metastatic melanoma and other solid tumors, are associated with immune-related adverse events, including ocular inflammation. We review the current literature on immune checkpoint inhibitor-associated uveitis (ICIPU). A comprehensive literature review utilizing MEDLINE/PubMed, Cochrane, and Web of Science databases was conducted.

MACULAR INFARCTION IN A PATIENT WITH SICKLE CELL TRAIT.

Purpose: Sickle cell trait affects 8% of African Americans. Once believed to represent a benign carrier state, it has been linked to an increased risk of several of the pathological conditions that arise in sickle cell disease in at-risk individuals with hematologic and vascular comorbidities. Macular infarction is a known complication of sickle cell disease; this article illustrates this unique presentation in a patient with sickle cell trait.

Connectomics of the zebrafish's lateral-line neuromast reveals wiring and miswiring in a simple microcircuit.

The lateral-line neuromast of the zebrafish displays a restricted, consistent pattern of innervation that facilitates the comparison of microcircuits across individuals, developmental stages, and genotypes. We used serial blockface scanning electron microscopy to determine from multiple specimens the neuromast connectome, a comprehensive set of connections between hair cells and afferent and efferent nerve fibers. This analysis delineated a complex but consistent wiring pattern with three striking characteristics: each nerve terminal is highly specific in receiving innervation from hair cells of a single directional sensitivity; the innervation is redundant; and the terminals manifest a hierarchy of dominance.

SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY FINDINGS IN COATS DISEASE.

Purpose: To evaluate microstructural retinal abnormalities on spectral domain optical coherence tomography (SD-OCT) imaging of eyes with Coats disease.

Methods: This is a multicenter, retrospective study in which SD-OCT images of patients with treatment-naive Coats disease were correlated with clinical examination and visual acuity and, when available, followed longitudinally over time.

Results: Macular SD-OCT of 27 eyes with Coats disease revealed intraretinal edema (59%), intraretinal exudates (67%), subretinal fluid (37%), subretinal exudate (48%), ellipsoid zone disruption (52%), external limiting membrane disruption (41%), and subfoveal nodule (26%).

Low temperatures during ontogeny increase fluctuating asymmetry and reduce maternal aggression in the house mouse, .

Maternal aggression is behavior displayed by post-partum lactating female mice toward unfamiliar conspecifics, presumably as a defense against infanticide. A variety of perinatal stressors can impair maternal care in adulthood. Previous studies on associations between developmental perturbations and maternal aggression have produced mixed results.

Cellular projections from sensory hair cells form polarity-specific scaffolds during synaptogenesis.

The assembly of a nervous system requires the extension of axons and dendrites to specific regions where they are matched with appropriate synaptic targets. Although the cues that guide long-range outgrowth have been characterized extensively, additional mechanisms are required to explain short-range guidance in neural development. Using a complementary combination of time-lapse imaging by fluorescence confocal microscopy and serial block-face electron microscopy, we identified a novel type of presynaptic projection that participates in the assembly of the vertebrate nervous system.

Generation and behavioral characterization of beta-catenin forebrain-specific conditional knock-out mice.

The canonical Wnt pathway and beta-catenin have been implicated in the pathophysiology of mood disorders. We generated forebrain-specific CRE-mediated conditional beta-catenin knock-out mice to begin exploring the behavioral implications of decreased Wnt pathway signaling in the central nervous system. In situ hybridization revealed a progressive knock-out of beta-catenin that began between 2 and 4 weeks of age, and by 12 weeks resulted in considerably decreased beta-catenin expression in regions of the forebrain, including the frontal cortex, hippocampus, and striatum.

Neonatal exposure to short days and low temperatures blunts stress response and yields low fluctuating asymmetry in Siberian hamsters.

Fluctuating asymmetry (FA) refers to small, non-directional deviations from perfect bilateral symmetry in morphological characters. Individuals with low FA presumably either developed in a relatively stable environment and/or were better able to buffer against developmental stressors. The present study investigated the effects of seasonal factors measured by day length and ambient temperature manipulations on the development of bilateral characters and concomitant changes in stress responses.

Maternal aggression persists following lipopolysaccharide-induced activation of the immune system.

Lactating females direct aggressive behaviors towards intruders presumably to reduce the likelihood of infanticide of their pups. Infected animals display a constellation of responses that include lethargy, anorexia, and decreased social interactions. This suite of responses is referred to as sickness behavior, and is putatively part of an adaptive strategy to aid the organism in recovery from infection.