Automated detection of hyperreflective foci in the outer nuclear layer of the retina

PURPOSE: Hyperreflective foci are poorly understood transient elements seen on optical coherence tomography (OCT) of the retina in both healthy and diseased eyes. Systematic studies may benefit from the development of automated tools that can map and track such foci. The outer nuclear layer (ONL) of the retina is an attractive layer in which to study hyperreflective foci as it has no fixed hyperreflective elements in healthy eyes. In this study, we intended to evaluate whether automated image analysis can identify, quantify and visualize hyperreflective foci in the ONL of the retina. METHODS: This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non-proliferative diabetic retinopathy. In total, 2596 OCT B-scan were obtained. An image analysis blob detector algorithm was used to detect candidate foci, and a convolutional neural network (CNN) trained on a manually labelled subset of data was then used to select those candidate foci in the ONL that fitted the characteristics of the reference foci best. RESULTS: In the manually labelled data set, the blob detector found 2548 candidate foci, correctly detecting 350 (89%) out of 391 manually labelled reference foci. The accuracy of CNN classifier was assessed by manually splitting the 2548 candidate foci into a training and validation set. On the validation set, the classifier obtained an accuracy of 96.3%, a sensitivity of 88.4% and a specificity of 97.5% (AUC 0.989). CONCLUSION: This study demonstrated that automated image analysis and machine learning methods can be used to successfully identify, quantify and visualize hyperreflective foci in the ONL of the retina on OCT scans

Multifocal visual evoked potentials in optic neuritis and multiple sclerosis: A review

The purpose of this review is to present a thorough survey of the results obtained by mf-VEP in ON and MS patients including comparisons to other measurements of the visual system (e.g. ff-VEP, OCT, visual functions, MRI), ON and MS disease course and; disability. The aim is to evaluate whether mf-VEP can be applied as a valuable method to detect visual pathway involvement in ON and MS and to monitor long term disease course

New biomarkers in inflammatory demyelinating diseases of the central nervous system (CNS)

The myelin oligodendrocyte glycoprotein (MOG) is a membrane protein expressed on the outmost surface of the myelin sheath in the central nervous system (CNS). Generally, the diagnostic, prognostic and therapeutic implications of anti-MOG-IgG in inflammatory demyelinating diseases in the CNS are unexplained

Functional-structural assessment of the optic pathways in patients with optic neuritis

The aim of this study is to evaluate whether spectral domain optical coherence tomography (SD-OCT) and multifocal visual evoked potential (mfVEP) are potentially better biomarkers than conventional fuel field visual evoked potential (ffVEP) in diagnosing Optic Neuritis (ON)

Automated detection of hyperreflective foci in the outer nuclear layer of the retina

Purpose: Hyperreflective foci are poorly understood transient elements seen on optical coherence tomography (OCT) of the retina in both healthy and diseased eyes. Systematic studies may benefit from the development of automated tools that can map and track such foci. The outer nuclear layer (ONL) of the retina is an attractive layer in which to study hyperreflective foci as it has no fixed hyperreflective elements in healthy eyes. In this study, we intended to evaluate whether automated image analysis can identify, quantify and visualize hyperreflective foci in the ONL of the retina. Methods: This longitudinal exploratory study investigated 14 eyes of seven patients including six patients with optic neuropathy and one with mild non-proliferative diabetic retinopathy. In total, 2596 OCT B-scan were obtained. An image analysis blob detector algorithm was used to detect candidate foci, and a convolutional neural network (CNN) trained on a manually labelled subset of data was then used to select those candidate foci in the ONL that fitted the characteristics of the reference foci best. Results In the manually labelled data set, the blob detector found 2548 candidate foci, correctly detecting 350 (89%) out of 391 manually labelled reference foci. The accuracy of CNN classifier was assessed by manually splitting the 2548 candidate foci into a training and validation set. On the validation set, the classifier obtained an accuracy of 96.3%, a sensitivity of 88.4% and a specificity of 97.5% (AUC 0.989). Conclusion: This study demonstrated that automated image analysis and machine learning methods can be used to successfully identify, quantify and visualize hyperreflective foci in the ONL of the retina on OCT scans.</p