2021 Dec 26

The visual system offers unparalleled precision in the assessment of neuroaxonal damage. With the majority of patients with multiple sclerosis (MS) experiencing afferent and efferent visual dysfunction, outcome measures capturing these deficits provide insight into neuroaxonal injury, even in those with minimal disability. Ideal for use in clinical trials, visual measures are generally inexpensive, accessible, and reproducible. Quantification of visual acuity, visual fields, visual quality of life, and electrophysiologic parameters allows assessment of function, whereas optical coherence tomography (OCT) provides reliable measures of the structural integrity of the anterior afferent visual pathway. The technology of oculomotor biometrics continues to advance, and discrete measures of fixation, smooth pursuit, and saccadic eye movement abnormalities are ready for inclusion in future trials of MS progression. Visual outcomes allow tracking of neuroaxonal injury and aid in distinguishing MS from diseases such as neuromyelitis optica spectrum disorder (NMOSD) or myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD). OCT has also provided unique insights into pathophysiology, including the identification of foveal pitting in NMOSD, possibly from damage to Müller cells, which carry an abundance of aquaporin-4 channels. For some study designs, the cost-benefit ratio favors visual outcomes over more expensive MRI outcomes. With the next frontier of therapeutics focused on remyelination and neuroprotection, visual outcomes are likely to take center stage. As an international community of collaborative, committed, vision scientists, this review by the International MS Visual System Consortium (IMSVISUAL) outlines the quality standards, informatics, and framework needed to routinely incorporate vision outcomes into MS and NMOSD trials.

2021 Jul 13

To update the consensus recommendations for reporting of quantitative optical coherence tomography (OCT) study results, thus revising the previously published Advised Protocol for OCT Study Terminology and Elements (APOSTEL) recommendations.

2021 May 19

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.