Neuro-Ophthalmic Manifestations of Adult Polyglucosan Body Disease.

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.

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.

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: 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.

MICK (Mobile Integrated Cognitive Kit) App for Concussion Assessment in a Youth Ice Hockey League.

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.

Testing the Validity and Reliability of a Standardized Virtual Examination for Concussion.

Background and Objectives: 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: 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: For total scores of the Tele-BCPE, both inter-modality agreement (ICC = 0.95 [95% CI 0.86-0.98, p < 0.001]) and inter-rater agreement (ICC = 0.88 [95% CI 0.71-0.95, p < 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: 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.