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Background Distal medium vessel occlusions (DMVOs) represent 25–40% of all acute ischemic strokes (AIS). DMVO clinical syndromes are heterogenous, but as eloquent brain regions are frequently involved, they are often disabling. Since current intravenous fibrinolytic therapies may fail to recanalize up to two-thirds of DMVOs, endovascular treatment is progressively being considered in this setting. Nevertheless, the optimal imaging method for diagnosis remains to be defined. Stroke centers that use computed tomography as a routine stroke imaging approach rely on either isolated computed tomography angiography (CTA) or combined perfusion (CTP) studies. Despite a simplified non-CTP-dependent approach seeming reasonable for large vessel occlusion AIS diagnosis, CTP may still hold advantages for DMVOs workup. Therefore, this systematic review aims to compare the diagnostic performance of CTA and CTP in detecting DMVOs. Methods We will perform a systematic search in PubMed, EMBASE, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials. In addition, grey literature and ClinicalTrials.gov will be scanned. We will include any type of study that presents data on the diagnostic accuracy of CTA and/or CTP for detecting DMVOs. Two authors will independently review retrieved studies, and any discrepancies will be resolved by consensus or with a third reviewer. Reviewers will extract the data and assess the risk of bias in the selected studies. Data will be combined in a quantitative meta-analysis following the guidelines provided by the Cochrane Handbook for Systematic Reviews of Interventions. We will assess cumulative evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. Discussion This will be the first systematic review and meta-analysis that compares two different imaging approaches for detecting DMVOs. This study may help to define optimal acute ischemic stroke imaging work-up. Trial registration PROSPERO registration:CRD42022344006.

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.