Journal of Neuro-Ophthalmology最新文献

Background: Retinal ganglion cells (RGCs) are diverse. Various types play specialized roles in vision, and they may be differentially susceptible in optic nerve disease where their death causes vision loss. RGCs are accordingly compelling targets for novel therapeutic strategies, and so it is clinically imperative to be able to evaluate different types individually in the human eye. This is complex and represents an unmet need for both basic and clinical research. We explore this need, survey emerging approaches, and consider their translational potential.

Methods: We conducted focused searches of online databases (PubMed, Embase, and Google Scholar) using relevant search terms for articles published until January 2025, screened abstracts for relevant publications, and citation searched discovered literature.

Results: Many approaches exist to classify human RGCs into types. Evidence suggests that some types are differentially susceptible to ocular disease, but these patterns are not firmly understood. Methods are emerging to evaluate individual RGC types in the human retina, alongside novel, potentially sight-restoring therapies that will depend on these insights for their full realization.

Conclusions: An integrated classification of RGC types, and refinement of methods to assess their status in the human eye, is clinically vital. Uncovering their roles can inform our understanding of disease biology, nominate biomarkers, and assist the development of therapies that protect, repair, or replace RGCs. The ongoing development of these techniques is imperative to the success of novel therapies for ocular disease.

Background: Multimodal artificial intelligence (AI) models have recently expanded into video analysis. In ophthalmology, one exploratory application is the automated detection of extraocular movement (EOM) disorders. This proof-of-concept study evaluated the feasibility of using Gemini 2.0 to recognize EOM abnormalities, identify the affected eye, and recognize specific movement limitations from publicly available, real-world clinical videos.

Methods: We retrospectively collected 114 YouTube videos of EOM disorders, including cranial nerve (CN) palsies, internuclear ophthalmoplegia (INO), supranuclear disorders, nystagmus, and ocular myasthenia gravis (MG), alongside 15 control videos demonstrating normal EOMs. Videos were trimmed to include only the pertinent clinical examinations, and audio was removed to avoid diagnostic cues. Using a standardized zero-shot prompt, Gemini 2.0 analyzed each video via the Google AI Studio platform. Gemini 2.0 was evaluated based on its ability to provide the correct diagnosis, identify the affected eye, and recognize the specific movement limitation (if any). Descriptive statistics, Spearman correlations, and comparative analyses were used to assess performance.

Results: Gemini 2.0 correctly identified the primary diagnosis in 43 of 114 videos, yielding an overall diagnostic accuracy of 37.7%. Diagnostic performance varied by condition, with the highest accuracies observed in third nerve palsy (81.1%), INO (80.0%), sixth nerve palsy (66.7%), and ocular MG (20.0%), whereas normal EOMs were correctly classified in 93.3% of cases. In misclassified cases, the correct diagnosis appeared in the differential diagnosis in 15.5% of instances. Laterality was correctly identified in 26.5% of eligible cases overall, 73.1% among correctly diagnosed cases vs. 9.6% in misclassified ones. Similarly, movement limitations were accurately identified in 30.3% of eligible cases overall, with a marked increase to 88.5% accuracy in correctly diagnosed cases compared to 9.6% in misclassified cases. Longer videos moderately correlated with longer processing time (ρ = 0.55, P < 0.001). Significant correlations were observed between correct diagnosis and correct laterality identification (ρ = 0.45, P < 0.001), correct diagnosis and correct movement limitation identification (ρ = 0.61, P < 0.001), and laterality and movement limitation (ρ = 0.51, P < 0.001). Processing time averaged 11.0 seconds and correlated with video length (ρ = 0.55, P < 0.001).

Conclusions: This proof-of-concept study demonstrates the feasibility of using Gemini 2.0 for automated recognition of EOM abnormalities in clinical videos. Although performance was stronger in overt cases, overall diagnostic accuracy remains limited. Substantial validation on standardized, clinician-annotated datasets is needed before clinical application.

Background: Papilledema and other optic neuropathies are critical findings in neuro-ophthalmology that require timely and accurate diagnosis. This study evaluates the performance of a deep learning system (DLS) to identify papilledema and other optic neuropathies, when applied to a large dataset of retinal photographs acquired prospectively with a handheld nonmydriatic camera in a neuro-ophthalmology department.

Methods: International multiethnic, multicenter study including 20,533 retinal photographs (10,647 patients) from 31 centers worldwide. The training and internal validation datasets consisted of 18,981 mydriatic retinal photographs (9,830 patients) from 22 countries. The external testing dataset included 1,552 prospectively collected retinal photographs (817 patients) acquired with a handheld camera (Aurora, Optomed, Finland). The DLS segmented the optic disc and peripapillary region, then classified each photograph as 1/"papilledema," 2/"other" optic disc abnormalities (i.e., swelling because of other causes, atrophy, etc.), or 3/"normal." The performance of the DLS in classifying optic disc appearance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Diagnostic outcomes were evaluated at the eye level and at the patient level.

Results: The DLS achieved an accuracy of 99.5% (95% confidence interval [CI], 99.1-99.8), a sensitivity of 81.0% (95% CI, 64.1-97.7), a specificity of 99.7% (95% CI, 99.5-99.9), and an AUC of 98.3% (95% CI, 97.6-99.9) for differentiating papilledema from "others" and healthy controls.

Conclusions: A DLS trained on a large dataset of mydriatic photographs achieved excellent diagnostic performance for detection of papilledema and other optic disc abnormalities when applied to nonmydriatic retinal photographs acquired with a handheld camera in real life conditions.

Background: We investigated the initial macular ganglion cell complex (GCC) changes in a group of patients during the conversion from normal vision to visual loss from Leber hereditary optic neuropathy (LHON).

Methods: Optical coherence tomography scans from 17 eyes of 10 patients with genetically confirmed LHON obtained within 12 weeks of visual symptom onset were analyzed. The thickness of the GCC was measured in 6 macular sectors: superior nasal (SN), inferior nasal (IN), superior temporal, inferior temporal (IT), superior (SUP), and inferior (INF). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUROC) analyses with DeLong comparison were used to evaluate the predictive reliability of GCC thinning in each sector.

Results: Selective, nasal (perifoveal) GCC thinning was observed in most LHON eyes during the early stages, with the SN and IN sectors showing the highest AUROC values. Thinning in these sectors provided significantly greater predictive value for the presence of LHON than other sectors.

Conclusions: Nasal GCC thinning is a reliable early indicator of the conversion from normal vision to visual loss in LHON as demonstrated in 10 of 17 eyes. This pattern of GCC loss provides insights into the disease mechanism and highlights the utility of OCT analysis for early diagnosis. The nasal GCC loss suggests selective vulnerability of ganglion cells serving the foveal region in LHON. Early identification of nasal GCC changes may facilitate timely intervention as gene therapy and other treatments become increasingly accessible.

Background: Neuromyelitis optica spectrum disorders (NMOSD) and myelin oligodendrocyte glycoprotein antibody disease (MOGAD) are autoimmune inflammatory disorders of the central nervous system. Central serous chorioretinopathy (CSCR) is characterized by a serous retinal detachment with leakage of fluid through the retinal pigment epithelium accumulating under the retina. This study investigated a potential association between CSCR and these neuroinflammatory disorders.

Methods: We included people with aquaporin-4 immunoglobulin G (AQP4-IgG) seropositive NMOSD (N = 39), multiple sclerosis (MS, N = 39), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD, N = 13), seronegative NMOSD (SN-NMOSD, N = 9), and healthy controls (HC, N = 30). Using optical coherence tomography (OCT), we assessed CSCR frequency and the thickness of the peripapillary retinal nerve fiber layer (pRNFL).

Results: There was a higher CSCR frequency (21.3%) throughout all investigated subgroups (AQP4-IgG seropositive NMOSD, MOGAD, and SN-NMOSD) than in the HC group (p = 0.048), with a significant association between CSCR and arterial hypertension frequency but not with these diagnoses, retinal neuroaxonal loss, or history of optic neuritis.

Conclusion: The high frequency of CSCR suggests a potential benefit of routine monitoring of CSCR in patients with NMOSD and MOGAD using the OCT technology, a reliable method to detect and monitor CSCR in patients with neuroinflammatory disorders. Further research is necessary to establish the underlying pathophysiology and potential effects on vision.

Background: Ocular myasthenia gravis (OMG) is an autoimmune disease characterized by autoantibodies targeting postsynaptic proteins at the neuromuscular junction, leading to weakness and fatigability of the levator palpebrae superioris, orbicularis oculi and extraocular muscles. Although OMG is primarily a clinical diagnosis, serological antibody testing, predominantly acetylcholine receptor (AChR) antibodies, is usually performed. The clinical utility of muscle-specific kinase (MuSK) antibodies is less well established in OMG. This meta-analysis evaluates the use of anti-AChR and anti-MuSK in patients with OMG and the relative costs of simultaneous vs sequential testing.

Methods: Studies were extracted from Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase (Ovid), Medline (Ovid), and additional gray literature. A systematic review was conducted using Covidence with 2 independent reviewers for study selection and data extraction. The meta-analysis was conducted with R version 4.4.1 on RStudio, and the meta package. Depending on the level of heterogeneity, either a fixed-effects or random-effects model was used to pool the data. Funnel plots were used to assess publication bias.

Results: The pooled analysis of 44 studies (n = 4,937 patients with OMG) revealed 59% (95% confidence interval [CI]: 52%-66%) positivity for anti-AChR, whereas the pooled analysis of 34 studies with (n = 3,380) showed 5% (95% CI: 2%-9%) positivity for anti-MuSK. From 62 studies (n = 5,180), 4 patients (0.1%) were doubly seropositive for anti-AChR and anti-MuSK. In patients with OMG positive for AChR antibodies, 5 studies (n = 527) reported a thymoma prevalence of 35% (95% CI: 3%-90%), underscoring the clinical value of anti-AChR testing. Four studies (n= 259) showed that anti-AChR positive patients had a 1.82 (95% CI: 1.15-2.88) times greater risk of progressing from OMG to generalized myasthenia gravis.

Conclusions: Almost two-thirds (59%) of the patients with OMG tested positive for AChR antibodies, but MuSK antibodies were only detected in 5% of patients. Positivity for anti-AChR in OMG was associated with a worse prognosis, including a higher prevalence of thymomas and an increased risk of disease generalization. Given the relatively low prevalence of anti-MuSK and the higher cost of anti-MuSK testing, clinicians could consider a stepwise approach to the serological diagnosis of OMG, where anti-MuSK is ordered only if the initial anti-AChR returns negative.