Genetic susceptibility to optic neuropathy in patients with alcohol use disorder.

BACKGROUND: The pathophysiology of toxico-nutritional optic neuropathies remains debated, with no clear understanding of the respective roles played by the direct alcohol toxicity, smoking and the often associated vitamin deficiencies, which are risk factors for optic neuropathy. Our aim was to investigate genetic susceptibility in patients with bilateral infraclinical optic neuropathy associated with chronic alcohol use disorder. METHODS: This retrospective cohort study included 102 visually asymptomatic patients with documented alcohol use disorder from a French reference center. Optic neuropathy was identified with optical coherence tomography (OCT), after which genetic susceptibility in the group of affected patients was investigated. Genetic testing was performed using panel sequencing of 87 nuclear genes and complete mitochondrial DNA sequencing. RESULTS: Optic neuropathy was detected in 36% (37/102) of the included patients. Genetic testing of affected patients disclosed two patients (2/30, 6.7%) with optic neuropathy associated with pathogenic variants affecting the SPG7 gene and five patients (5/30, 16.7%) who harbored variants of uncertain significance close to probable pathogenicity in the genes WFS1, LOXL1, MMP19, NR2F1 and PMPCA. No pathogenic mitochondrial DNA variants were found in this group. CONCLUSIONS: OCT can detect presence of asymptomatic optic neuropathy in patients with chronic alcohol use disorder. Furthermore, genetic susceptibility to optic neuropathy in this setting is found in almost a quarter of affected patients. Further studies may clarify the role of preventative measures in patients who might be predisposed to avoidable visual loss and blindness.

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs.

BACKGROUND: Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test a dedicated deep learning system (DLS) for binary classification of ODD vs papilledema (including various subgroups within each category), on conventional mydriatic digital ocular fundus photographs collected in a large international multiethnic population. METHODS: This retrospective study included 4,508 color fundus images in 2,180 patients from 30 neuro-ophthalmology centers (19 countries) participating in the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) Group. For training and internal validation, we used 857 ODD images and 3,230 papilledema images, in 1,959 patients. External testing was performed on an independent data set (221 patients), including 207 images with ODD (96 visible and 111 buried), provided by 3 centers of the Optic Disc Drusen Studies Consortium, and 214 images of papilledema (92 mild-to-moderate and 122 severe) from a previously validated study. RESULTS: The DLS could accurately distinguish between all ODD and papilledema (all severities included): area under the receiver operating characteristic curve (AUC) 0.97 (95% confidence interval [CI], 0.96-0.98), accuracy 90.5% (95% CI, 88.0%-92.9%), sensitivity 86.0% (95% CI, 82.1%-90.1%), and specificity 94.9% (95% CI, 92.3%-97.6%). The performance of the DLS remained high for discrimination of buried ODD from mild-to-moderate papilledema: AUC 0.93 (95% CI, 0.90-0.96), accuracy 84.2% (95% CI, 80.2%-88.6%), sensitivity 78.4% (95% CI, 72.2%-84.7%), and specificity 91.3% (95% CI, 87.0%-96.4%). CONCLUSIONS: A dedicated DLS can accurately distinguish between ODD and papilledema caused by intracranial hypertension, even when considering buried ODD vs mild-to-moderate papilledema.

Basics, benefits, and pitfalls of pupillometers assessing visual function.

Numerous commercially and non-commercially available pupillometers are nowadays able to assess various biological functions in humans, by evaluating pupils' dynamics in response to specific stimuli. However, the use of pupillometers for ophthalmic afferent evaluations (i.e., photoreceptoral responses) in real-world settings is relatively limited. Recent scientific and technological advances, coupled with artificial intelligence methods have improved the performance of such devices to objectively detect, quantify, and classify functional disturbances of the retina and the optic nerve. This review aims to summarize the scientific principles, indications, outcomes, and current limitations of pupillometry used for evaluation of afferent pathways in ophthalmic clinical settings.

The BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) deep learning system can accurately identify pediatric papilledema on standard ocular fundus photographs.

BACKGROUND: Pediatric papilledema often reflects an underlying severe neurologic disorder and may be difficult to appreciate, especially in young children. Ocular fundus photographs are easy to obtain even in young children and in nonophthalmology settings. The aim of our study was to ascertain whether an improved deep-learning system (DLS), previously validated in adults, can accurately identify papilledema and other optic disk abnormalities in children. METHODS: The DLS was tested on mydriatic fundus photographs obtained in a multiethnic pediatric population (<17 years) from three centers (Atlanta-USA; Bucharest-Romania; Singapore). The DLS's multiclass classification accuracy (ie, normal optic disk, papilledema, disks with other abnormality) was calculated, and the DLS's performance to specifically detect papilledema and normal disks was evaluated in a one-vs-rest strategy using the AUC, sensitivity and specificity, with reference to expert neuro-ophthalmologists. RESULTS: External testing was performed on 898 fundus photographs: 447 patients; mean age, 10.33 (231 patients ≤10 years of age; 216, 11-16 years); 558 normal disks, 254 papilledema, 86 other disk abnormalities. Overall multiclass accuracy of the DLS was 89.6% (range, 87.8%-91.6%). The DLS successfully distinguished "normal" from "abnormal" optic disks (AUC 0.99 [0.98-0.99]; sensitivity, 87.3% [84.9%-89.8%]; specificity, 98.5% [97.6%-99.6%]), and "papilledema" from "normal and other" (AUC 0.99 [0.98-1.0]; sensitivity, 98.0% [96.8%-99.4%]; specificity, 94.1% (92.4%-95.9%)]. CONCLUSIONS: Our DLS reliably distinguished papilledema from normal optic disks and other disk abnormalities in children, suggesting it could be utilized as a diagnostic aid for the assessment of optic nerve head appearance in the pediatric age group.

Value Proposition of Retinal Imaging in Alzheimer's Disease Screening: A Review of Eight Evolving Trends.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Current diagnostic modalities of AD generally focus on detecting the presence of amyloid ß and tau protein in the brain (for example, positron emission tomography [PET] and cerebrospinal fluid testing), but these are limited by their high cost, invasiveness, and lack of expertise. Retinal imaging exhibits potential in AD screening and risk stratification, as the retina provides a platform for the optical visualization of the central nervous system in vivo, with vascular and neuronal changes that mirror brain pathology. Given the paradigm shift brought by advances in artificial intelligence and the emergence of disease-modifying therapies, this article aims to summarize and review the current literature to highlight 8 trends in an evolving landscape regarding the role and potential value of retinal imaging in AD screening.