Combined multi-modal assessment of glaucomatous damage with electroretinography and optical coherence tomography/angiography

Purpose: To compare the diagnostic performance and to evaluate the interrelationship of electroretinographical and structural and vascular measures in glaucoma. Methods: For 14 eyes of 14 healthy controls and 15 eyes of 12 patients with glaucoma ranging from preperimetric to advanced stages optical coherence tomog-raphy (OCT), OCT-angiography (OCT-A), and electrophysiological measures (multifocal photopic negative response ratio [mfPhNR] and steady-state pattern electroretinogra-phy [ssPERG]) were applied to assess changes in retinal structure, microvasculature, and function, respectively. The diagnostic performance was assessed via area-under-curve (AUC) measures obtained from receiver operating characteristics analyses. The interre-lation of the different measures was assessed with correlation analyses. Results: The mfPhNR, ssPERG amplitude, parafoveal (pfVD) and peripapillary vessel density (pVD), macular ganglion cell inner plexiform layer thickness (mGCIPL) and peripapillary retinal nerve fiber layer thickness (pRNFL) were significantly reduced in glaucoma. The AUC for mfPhNR was highest among diagnostic modalities (AUC: 0.88, 95% confidence interval: 0.75–1.0, P < 0.001), albeit not statistically different from that for macular (mGCIPL: 0.76, 0.58–0.94, P < 0.05; pfVD: 0.81, 0.65–0.97, P < 0.01) or peripapillary imaging (pRNFL: 0.85, 0.70–1.0, P < 0.01; pVD: 0.82, 0.68–0.97, P < 0.01). Combined functional/vascular measures yielded the highest AUC (mfPhNR-pfVD: 0.94, 0.85–1.0, P < 0.001). The functional/structural measure correlation (mfPhNR-mGCIPL correlation coefficient [rs ]: 0.58, P = 0.001; mfPhNR-pRNFL rs: 0.66, P < 0.001) was stronger than the functional-vascular correlation (mfPhNR-pfVD rs: 0.29, P = 0.13; mfPhNR-pVD rs: 0.54, P = 0.003). Conclusions: The combination of ERG measures and OCT-A improved diagnostic performance and enhanced understanding of pathophysiology in glaucoma. Translational Relevance: Multimodal assessment of glaucoma damage improves diagnostics and monitoring of disease progression

CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism

Purpose: Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose.Methods: We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net.Results: The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts.Conclusions: CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics

Tracking the visual system—from the optic chiasm to primary visual cortex

&lt;jats:title&gt;Abstract&lt;/jats:title&gt;&lt;jats:p&gt;Epilepsy surgery is a well-established method of treatment for pharmacoresistant focal epilepsies, but it carries an inherent risk of damaging eloquent brain structures. This holds true in particular for visual system pathways, where the damage to, for example, the optic radiation may result in postoperative visual field defects. Such risk can be minimized by the identification and localization of visual pathways using diffusion magnetic resonance imaging (dMRI). The aim of this article is to provide an overview of the step-by-step process of reconstructing the visual pathways applying dMRI analysis. This includes data acquisition, preprocessing, identification of key structures of the visual system necessary for reconstruction, as well as diffusion modeling and the ultimate reconstruction of neural pathways. As a result, the reader will become familiar both with the ideas and challenges of imaging the visual system using dMRI and their relevance for planning the intervention. &lt;/jats:p&gt

CHIASM-Net: Artificial Intelligence-Based Direct Identification of Chiasmal Abnormalities in Albinism

Purpose: Albinism is a congenital disorder affecting pigmentation levels, structure, and function of the visual system. The identification of anatomical changes typical for people with albinism (PWA), such as optic chiasm malformations, could become an important component of diagnostics. Here, we tested an application of convolutional neural networks (CNNs) for this purpose. Methods: We established and evaluated a CNN, referred to as CHIASM-Net, for the detection of chiasmal malformations from anatomic magnetic resonance (MR) images of the brain. CHIASM-Net, composed of encoding and classification modules, was developed using MR images of controls (n = 1708) and PWA (n = 32). Evaluation involved 8-fold cross validation involving accuracy, precision, recall, and F1-score metrics and was performed on a subset of controls and PWA samples excluded from the training. In addition to quantitative metrics, we used Explainable AI (XAI) methods that granted insights into factors driving the predictions of CHIASM-Net. Results: The results for the scenario indicated an accuracy of 85 ± 14%, precision of 90 ± 14% and recall of 81 ± 18%. XAI methods revealed that the predictions of CHIASM-Net are driven by optic-chiasm white matter and by the optic tracts. Conclusions: CHIASM-Net was demonstrated to use relevant regions of the optic chiasm for albinism detection from magnetic resonance imaging (MRI) brain anatomies. This indicates the strong potential of CNN-based approaches for visual pathway analysis and ultimately diagnostics.</p