Faculty Bibliography

Optic disc swelling, frequently associated with vitamin A toxicity, is infrequently linked to vitamin A deficiency. This report describes a 6-year-old male with autism spectrum disorder (ASD) and avoidant restrictive food intake disorder who presented with xerophthalmia, optic disc swelling, vision changes, and deficiencies in vitamins A, B₁, and iron. The patient's behavioral dysregulation posed important challenges for the evaluation, diagnosis, and treatment of his hypovitaminoses. This case underscores the importance of considering multiple nutritional deficiencies as the etiology of optic disc swelling in pediatric populations with autism spectrum disorder and avoidant restrictive food intake disorder, diagnoses that have increased in frequency. Early recognition and intervention can prevent further complications such as visual loss and improve outcomes.

BACKGROUND:In many parts of the world including India, the prevalence of autoimmune inflammatory diseases such as neuromyelitis optica spectrum disorders (NMOSD), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and multiple sclerosis (MS) is rising. A diagnosis is often delayed due to insufficient diagnostic tools. Machine learning (ML) models have accurately differentiated eyes of patients with MS from those of healthy controls (HCs) using optical coherence tomography (OCT)-based retinal images. Examining OCT characteristics may allow for early differentiation of these conditions. The objective of this study was to determine feasibility of ML analyses to distinguish between patients with different autoimmune inflammatory diseases, other ocular diseases, and HCs based on OCT measurements of the peripapillary retinal nerve fiber layer (pRNFL), ganglion cell-inner plexiform layer (GCIPL), and inner nuclear layers (INLs). METHODS:Eyes of people with MS (n = 99 patients), NMOSD (n = 40), MOGAD (n = 74), other ocular diseases (OTHER, n = 16), and HCs (n = 54) from the Mangalore Demyelinating Disease Registry were included. Support vector machine (SVM) classification models incorporating age, pRNFL, GCIPL, and INL were performed. Data were split into training (70%) and testing (30%) data and accounted for within-patient correlations. Cross-validation was used in training to choose the best parameters for the SVM model. Accuracy and area under receiver operating characteristic curves (AUROCs) were used to assess model performance. RESULTS:The SVM models distinguished between eyes of patients with each condition (i.e., MOGAD vs NMOSD, NMOSD vs HC, MS vs OTHER, etc) with strong discriminatory power demonstrated from the AUROCs for these comparisons ranging from 0.81 to 1.00. These models also performed with moderate to high accuracy, ranging from 0.66 to 0.81, with the exception of the MS vs NMOSD comparison, which had an accuracy of 0.53. CONCLUSIONS:ML models are useful for distinguishing between autoimmune inflammatory diseases and for distinguishing these from HCs and other ocular diseases based on OCT measures. This study lays the groundwork for future deep learning studies that use analyses of raw OCT images for identifying eyes of patients with such disorders and other etiologies of optic neuropathy.

BACKGROUND:Visual symptoms are common after concussion. Rapid automatized naming (RAN) tasks are simple performance measures that demonstrate worse time scores in the setting of acute or more remote injury. METHODS:We evaluated the capacity for the Mobile Universal Lexicon Evaluation System (MULES) and Staggered Uneven Number (SUN) testing to be feasibly administered during preseason testing in a cohort of youth ice hockey athletes using a novel computerized app, the Mobile Integrated Cognitive Kit (MICK). Participants from a youth hockey league underwent preseason testing. RESULTS:Among 60 participants, the median age was 13 years (range 6-17). The median best time for the MULES was 49.8 seconds (range = 34.2-141.0) and the median best time for the SUN was 70.1 (range = 36.6-200.0). As is characteristic of timed performance measures, there were learning effects between the first and second trials for both the MULES (median improvement = 10.6 seconds, range = -32.3 to 92.0, P < 0.001, Wilcoxon signed-rank test) and SUN (median improvement = 2.4 seconds, range= -8.0 to 15.1, P = 0.001, Wilcoxon signed-rank test). Age was a predictor of best baseline times, with longer (worse) times for younger participants for MULES (P < 0.001, rs = -0.67) and SUN (P < 0.001, rs = -0.54 Spearman rank correlation). Degrees of learning effect did not vary with age (P > 0.05, rs = -0.2). CONCLUSIONS:Vision-based RAN tasks, such as the MULES and SUN, can be feasibly administered using the MICK app during preseason baseline testing in youth sports teams. The results suggest that more frequent baseline tests are necessary for preadolescent athletes because of the relation of RAN task performance to age.

BACKGROUND:Adult polyglucosan body disease (APBD) is caused by a deficiency in glycogen branching enzyme that leads to polyglucosan accumulation in multiple organs. It has a progressive clinical course with prominent neurologic manifestations. We aim to describe the neuro-ophthalmic manifestations of APBD. METHODS:This is a case series of 3 individuals with genetically proven APBD. Written informed consent was provided by the brothers. We also performed a literature review on the current state of knowledge on APBD through PubMed. RESULTS:Brother 1 developed gait imbalance and length-dependent polyneuropathy in his 40s followed by progressive urinary symptoms in his 50s. He reported diplopia and blurry vision in his 60s. Neuro-ophthalmic assessment revealed bilateral optic neuropathy, convergence insufficiency, and a right fourth nerve palsy. Genetic testing showed a homozygous pathogenic variant in GBE1 c.986A>C p.Tyr329Ser. Brother 2 developed progressive urinary symptoms in his 40s that were followed by cognitive deficits, length-dependent polyneuropathy, and lower extremity weakness in his 50s and 60s. He reported blurred vision, and neuro-ophthalmic evaluation revealed bilateral optic neuropathy. Genetic testing revealed the same variant as Brother 1, GBE1 c.986A>C p.Tyr329Ser. Brother 3 developed progressive urinary urgency and lower extremity weakness in his 50s followed by a length-dependent polyneuropathy in his 60s. He reported diplopia and blurry vision in his 70s. Neuro-ophthalmic assessment revealed bilateral optic neuropathy and convergence insufficiency. Genetic testing revealed the same variant as Brothers 1 and 2, GBE1 c.986A>C p.Tyr329Ser. CONCLUSIONS:There is an array of afferent and efferent neuro-ophthalmic manifestations in APBD. Neuro-ophthalmic evaluation is crucial in evaluating and treating patients with APBD, particularly in those with visual dysfunction.

BACKGROUND:Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease. EVIDENCE ACQUISITION/METHODS:Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI. RESULTS:This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice. CONCLUSIONS:We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.

BACKGROUND AND OBJECTIVES/UNASSIGNED:We determined inter-modality (in-person vs telemedicine examination) and inter-rater agreement for telemedicine assessments (2 different examiners) using the Telemedicine Buffalo Concussion Physical Examination (Tele-BCPE), a standardized concussion examination designed for remote use. METHODS/UNASSIGNED:Patients referred for an initial evaluation for concussion were invited to participate. Participants had a brief initial assessment by the treating neurologist. After a patient granted informed consent to participate in the study, the treating neurologist obtained a concussion-related history before leaving the examination room. Using the Tele-BCPE, 2 virtual examinations in no specific sequence were then performed from nearby rooms by the treating neurologist and another neurologist. After the 2 telemedicine examinations, the treating physician returned to the examination room to perform the in-person examination. Intraclass correlation coefficients (ICC) determined inter-modality validity (in-person vs remote examination by the same examiner) and inter-rater reliability (between remote examinations done by 2 examiners) of overall scores of the Tele-BCPE within the comparison datasets. Cohen's kappa, κ, measured levels of agreement of dichotomous ratings (abnormality present vs absent) on individual components of the Tele-BCPE to determine inter-modality and inter-rater agreement. RESULTS/UNASSIGNED:< 0.001]) were reliable (ICC >0.70). There was at least substantial inter-modality agreement (κ ≥ 0.61) for 25 of 29 examination elements. For inter-rater agreement (2 telemedicine examinations), there was at least substantial agreement for 8 of 29 examination elements. DISCUSSION/UNASSIGNED:Our study demonstrates that the Tele-BCPE yielded consistent clinical results, whether conducted in-person or virtually by the same examiner, or when performed virtually by 2 different examiners. The Tele-BCPE is a valid indicator of neurologic examination findings as determined by an in-person concussion assessment. The Tele-BCPE may also be performed with excellent levels of reliability by neurologists with different training and backgrounds in the virtual setting. These findings suggest that a combination of in-person and telemedicine modalities, or involvement of 2 telemedicine examiners for the same patient, can provide consistent concussion assessments across the continuum of care.

Digital health technologies (DHTs) can transform neurological assessments, improving quality and continuity of care. In the United States, the Food & Drug Administration (FDA) oversees the safety and efficacy of these technologies, employing a detailed regulatory process that classifies devices based on risk and requires rigorous review and post-market surveillance. Following FDA approval, DHTs enter the Current Procedural Terminology, Relative Value Scale Update Committee, and Centers for Medicare & Medicaid Services coding and valuation processes leading to coverage and payment decisions. DHT adoption is challenged by rapid technologic advancements, an inconsistent evidence base, marketing discrepancies, ambiguous coding guidance, and variable health insurance coverage. Regulators, policymakers, and payers will need to develop better methods to evaluate these promising technologies and guide their deployment. This includes striking a balance between patient safety and clinical effectiveness versus promotion of innovation, especially as DHTs increasingly incorporate artificial intelligence. Data validity, cybersecurity, risk management, societal, and ethical responsibilities should be addressed. Regulatory advances can support adoption of these promising tools by ensuring DHTs are safe, effective, accessible, and equitable.

BACKGROUND:Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) is a demyelinating disorder that most commonly presents with optic neuritis (ON) and affects children more often than adults. We report 8 pediatric patients with MOG-associated ON and characterize focal optical coherence tomography (OCT) abnormalities over time that help distinguish this condition from the trajectories of other demyelinating disorders. These OCT findings are examined in the context of longitudinal visual function testing. METHODS:This is a retrospective case series of 8 pediatric patients with MOG-associated ON who were referred for neuro-ophthalmic evaluation. Longitudinal data for demographics, clinical history, physical examination, and OCT obtained in the course of clinical evaluations were collected through retrospective medical record review. RESULTS:Patients demonstrated acute peripapillary retinal nerve fiber layer (RNFL) thickening in one or both eyes, consistent with optic disc swelling. This was followed by steady patterns of average RNFL thinning, with 9 of 16 eyes reaching significantly low RNFL thickness using OCT platform reference databases (P < 0.01), accompanied by paradoxical recovery of high-contrast visual acuity (HCVA) in every patient. There was no correlation between HCVA and any OCT measures, although contrast sensitivity (CS) was associated with global thickness, PMB thickness, and nasal/temporal (N/T) ratio, and color vision was associated with PMB thickness. There was a lower global and papillomacular bundle (PMB) thickness (P < 0.01) in clinically affected eyes compared with unaffected eyes. There was also a significantly higher N:T ratio in clinically affected eyes compared with unaffected eyes in the acute MOG-ON setting (P = 0.03), but not in the long-term setting. CONCLUSIONS:MOG shows a pattern of prominent retinal atrophy, as demonstrated by global RNFL thinning, with remarkable preservation of HCVA but remaining deficits in CS and color vision. These tests may be better clinical markers of vision changes secondary to MOG-ON. Of the OCT parameters measured, PMB thickness demonstrated the most consistent correlation between structural and functional measures. Thus, it may be a more sensitive marker of clinically significant retinal atrophy in MOG-ON. The N:T ratio in acute clinically affected MOG-ON eyes in our study was higher than the N:T ratio of neuromyelitis optica (NMO)-ON eyes and similar to the N:T ratio in multiple sclerosis (MS)-ON eyes as presented in the prior literature. Therefore, MOG may share a more similar pathophysiology to MS compared with NMO.