No abstract

Clinical diagnoses of slow, progressive, painless visual losses with various degrees of visual field (VF) losses and disc atrophy are often confused between suprasellar compressive optic neuropathy (CON) and open-angle glaucomatous optic neuropathy (GON). We plotted the thickness of the peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) against the mean deviation (MD) of the VF of 34 eyes of CON at diagnosis, 30 eyes of CON after therapy, 29 eyes of GON, and 60 eyes of healthy controls in a cross-sectional investigation. At diagnosis, a disproportionally early pattern of structural thinning compared with the corresponding VF losses was unique to CON. GON- and CON-specific thinning parameters were generally useful in differentiating GON and CON from moderate to severe MD losses, but early MD losses (0 to − 6 dB) overlapped with GON in a CON-stage specific manner. GON-specific thinning parameters, RNFL in the inferior sector, and inferior to temporal macular GCIPL ratio showed overlap with posttreatment CON in the early MD losses with AUCs of 0.916 (95% CI 0.860–0.971; P < 0.001) and 0.890 (95% CI 0.811–0.968; P < 0.001), respectively. In comparison, CON-specific thinning parameters, superonasal, and inferonasal GCIPL showed overlap with CON at diagnosis for early MD losses. Overall, the nasal-to-temporal macular GCIPL ratio showed good discrimination between CON and GON throughout the MD range, with an AUC of 0.923 (95% CI 0.870–0.976; P < 0.001). Comparing GON with all stages of CON, the cut-point of 0.95 showed the lower nasal-to-temporal GCIPL ratio had a sensitivity of 72% and specificity of 90% for CON. However, the cut-point of 1.10 showed the superior-to-inferior GCIPL ratio had a sensitivity of 60% and specificity of 98% for GON.

Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice. Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema