Retinal Neuronal Loss in Visually Asymptomatic Patients With Myoclonic Epilepsy With Ragged-Red Fibers

BACKGROUND: Myoclonic epilepsy with ragged-red fibers (MERRF, OMIM, #545000) is a rare neurological condition mostly caused by the m.8344A>G mitochondrial DNA pathogenic variant, which can variably affect multiple tissues, including the retina and optic nerve. We report detection of visually asymptomatic neuroretinal loss in 3 patients with genetically confirmed MERRF, using spectral domain optical coherence tomography (SD-OCT). METHODS: All patients underwent a complete ophthalmic examination including assessments of visual acuity, color vision, pupillary reactions, extraocular movements, applanation tonometry, slit-lamp, and dilated fundus examinations. Standard automated perimetry or Goldmann kinetic perimetry was performed, as well as fundus photographs and SD-OCT of the optic nerve head and macula. RESULTS: Despite the absence of visual symptoms in all patients, and normal visual acuity and visual fields in 1 patient, the 3 genetically confirmed patients (point mutations m.8344A>G; age range: 18-62 years) with MERRF-related neurological manifestations, displayed thinning of the retinal nerve fiber layer and variable alterations of the macular ganglion cell complex. CONCLUSIONS: Visually asymptomatic patients with genetically confirmed MERRF can display features of structural neuroretinal loss, quantifiable with SD-OCT. Further investigations are needed to establish whether OCT can assess early neurodegeneration in MERRF

Retinal Neuronal Loss in Visually Asymptomatic Patients With Myoclonic Epilepsy With Ragged-Red Fibers

BACKGROUND: Myoclonic epilepsy with ragged-red fibers (MERRF, OMIM, #545000) is a rare neurological condition mostly caused by the m.8344A>G mitochondrial DNA pathogenic variant, which can variably affect multiple tissues, including the retina and optic nerve. We report detection of visually asymptomatic neuroretinal loss in 3 patients with genetically confirmed MERRF, using spectral domain optical coherence tomography (SD-OCT). METHODS: All patients underwent a complete ophthalmic examination including assessments of visual acuity, color vision, pupillary reactions, extraocular movements, applanation tonometry, slit-lamp, and dilated fundus examinations. Standard automated perimetry or Goldmann kinetic perimetry was performed, as well as fundus photographs and SD-OCT of the optic nerve head and macula. RESULTS: Despite the absence of visual symptoms in all patients, and normal visual acuity and visual fields in 1 patient, the 3 genetically confirmed patients (point mutations m.8344A>G; age range: 18-62 years) with MERRF-related neurological manifestations, displayed thinning of the retinal nerve fiber layer and variable alterations of the macular ganglion cell complex. CONCLUSIONS: Visually asymptomatic patients with genetically confirmed MERRF can display features of structural neuroretinal loss, quantifiable with SD-OCT. Further investigations are needed to establish whether OCT can assess early neurodegeneration in MERRF

Light-induced pupillary responses in Alzheimer's disease

10.3389/fneur.2019.00360Frontiers in Neurology1036

Association of time outdoors and patterns of light exposure with myopia in children

10.1136/bjophthalmol-2021-318918British Journal of OphthalmologyBJOP

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists

Objective: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. Methods: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. Results: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96\u20130.98), 0.96 (95% CI = 0.94\u20130.97), and 0.89 (95% CI = 0.87\u20130.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67\u20130.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68\u20130.76) between the system and Expert 1, and 0.65 (95% CI = 0.61\u20130.70) between the system and Expert 2. Interpretation: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785\u2013795