No abstract

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.

This article, co-authored by a patient affected by bilateral, recurrent, atypical optic neuritis, and clinicians, discusses the mental burden of living with uncertainty and the possibility of further sight loss, along with the side effects of treatment. The patient shares some of the challenges, coping strategies, and the value they found in creating and participating in a patient support group. The physicians consider whether current clinical measures adequately capture the outcomes that matter to patients and discuss the role for patient-reported outcome measures (PROMs). We identify technological advances that are lowering traditional barriers to the use of PROMs in research and routine clinical care and look towards new PROM instruments enhancing shared patient-physician care in the future.