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Purpose: The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method to facilitate diagnosis and prognosis in MS. Methods: This paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier. Results: For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs. Conclusion: We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method. [ABSTRACT FROM AUTHOR]

Purpose: To assess the use of deep learning (DL) for computer‐assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the explainability of the glaucoma classifier. Methods: This original investigation pooled 8433 retrospectively collected and anonymized colour optic disc‐centred fundus images, in order to develop a deep learning‐based classifier for glaucoma diagnosis. The labels of the various deep learning models were compared with the clinical assessment by glaucoma experts. Data were analysed between March and October 2018. Sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and amount of data used for discriminating between glaucomatous and non‐glaucomatous fundus images, on both image and patient level. Results: Trained using 2072 colour fundus images, representing 42% of the original training data, the trained DL model achieved an AUC of 0.995, sensitivity and specificity of, respectively, 98.0% (CI 95.5%–99.4%) and 91% (CI 84.0%–96.0%), for glaucoma versus non‐glaucoma patient referral. Conclusions: These results demonstrate the benefits of deep learning for automated glaucoma detection based on optic disc‐centred fundus images. The combined use of transfer and active learning in the medical community can optimize performance of DL models, while minimizing the labelling cost of domain‐specific mavens. Glaucoma experts are able to make use of heat maps generated by the deep learning classifier to assess its decision, which seems to be related to inferior and superior neuroretinal rim (within ONH), and RNFL in superotemporal and inferotemporal zones (outside ONH). [ABSTRACT FROM AUTHOR]

. Purpose: To compare clinicians and a trained artificial neural network (ANN) regarding accuracy and certainty of assessment of visual fields for the diagnosis of glaucoma. Methods: Thirty physicians with different levels of knowledge and experience in glaucoma management assessed 30-2 SITA Standard visual field printouts that included full Statpac information from 99 patients with glaucomatous optic neuropathy and 66 healthy subjects. Glaucomatous eyes with perimetric mean deviation values worsethan −10 dB were not eligible. The fields were graded on a scale of 1-10, where 1 indicated healthy with absolute certaintyand 10 signified glaucoma; 5.5 was the cut-off between healthy and glaucoma. The same fields were classified by a previously trained ANN. The ANN output was transformed into a linear scale that matched the scale used in the subjective assessments. Classification certainty was assessed using a classification error score. Results: Among the physicians, sensitivity ranged from 61% to 96% (mean 83%) and specificity from 59% to 100% (mean 90%). Our ANN achieved 93% sensitivity and 91% specificity, and it was significantly more sensitive than the physicians (p < 0.001) at a similar level of specificity. The ANN classification error score was equivalent to the top third scores of all physicians, and the ANN never indicated a high degree of certainty for any of its misclassified visual field tests. Conclusion: Our results indicate that a trained ANN performs at least as well as physicians in assessments of visual fields for the diagnosis of glaucoma. [ABSTRACT FROM AUTHOR]