EMVision Medical Devices Limited announced very encouraging findings from its pilot clinical trial. The single-site study, at the Princess Alexandra Hospital (PAH) in Brisbane, of patients with diagnosed ischaemic or haemorrhagic stroke, is the first clinical study for EMVision's novel imaging technology. The primary endpoint was the collection of a dataset of stroke patients which improves the understanding of stroke on electromagnetic scattering effects in the brain. This end point has been met, producing datasets that have enabled EMVision to advance its imaging algorithm development and observe the correlation of EMVision scans with "ground truth" CT and/or MRI scans. Furthermore, clinician and patient feedback on the usability and comfort of EMVision's clinical prototype has been collected. The contract research organisation (CRO) is Mobius Medical Pty Ltd. No intervention or modification to the standard of care of hospital-based treatment of stroke was done as part of this study. The scanner was not used for clinical evaluation or imaging. The study was designed to collect data to tune the EMVision algorithms. The de-identified patient ground truth CT/MRI "training sets" made it possible for the algorithm team to refine the imaging and classification algorithms. Additionally, clinical advisors in conjunction with the technical team identified five datasets where p athologies were estimated to fall outside of the anticipated prototype hardware range. Data has been presented below with, and without, the 5 excluded datasets for completeness. Due to the design of the study and smaller sample size (including a small number of haemorrhagic cases), the dataset does not enable statistically significant conclusions to be drawn on diagnostic sensitivity/specificity at this stage. The study enrolled and processed datasets from 30 patients (21 ischaemic and 9 haemorrhagic) representing the diversity of stroke in localisation, size and clinical severity. The mean age was 66.7 years of age with the majority, 70% of patients, aged 60 years and over. There were slightly fewer male patients (43.3%) than female (56.7%). Of the 30 patients, 19, (63.3%) had only a CT performed whereas 11, (36.7%) had CT/MRI performed. As a result of these scans, 30% of patients were diagnosed as having had a haemorrhagic stroke and 70% as having had an ischaemic stroke. National Institutes of Health Stroke Scale (NIHSS) was recorded. The NIHSS score is used to measure stroke severity. The mean NIHSS score was calculated as 5.2 which indicates mild severity. The participating patients' de-identified CT and/or MRI ground truth scans were interpreted and classified independently by EMVision clinical and radiology advisors. The EMVision device scans were acquired close to the timing of the corresponding ground truth scans. After the EMVision datasets were processed by the algorithm team, the algorithm classification and localisation outputs were verified by clinical advisors. Clinical Trial Results: The EMVision classification was observed to demonstrate an ability to differentiate between haemorrhagic and ischaemic stroke with an overall accuracy of 93.3% [95% CI 2] in the full sample (30) and 96% (95% CI) in the sample excluding patients with pathologies located outside the estimated prototype hardware range. Localisation has been evaluated based on whether the EMVision fusion images resulted in target detection in the same quadrant as the ground truth scans (CT/MRI). For any scenarios where the ground truth image or fusion image had multiple areas of pathology identified, the clinical verifier has taken the most prominent /intense area to be the area of interest. The EMVision fusion images were observed to be able to localise in the correct quadrant with an overall accuracy of 86.7% [95% CI] (full sample of 30 datasets) and 96% [95% CI] (sample with the 5 excluded datasets). The study datasets have enabled the algorithm team to advance the hybrid "fusion" methodology, which is a powerful approach to imaging. The fusion hybrid works by extracting the target lesion and estimated location in each algorithm and applies a pixel-wise voting algorithm. The fused image then leverages the classification algorithm and can be overlaid on a predicted head template. The algorithm team will continue to advance this fusion methodology in consultation with EMVision's clinical advisors. The fusion imaging approach is illustrated below with examples. Secondary endpoints for operator and patient feedback were reported by a Likert scale (1-5), with a score of 1 representing `strongly agree' and 5 representing `strongly disagree'. Despite being a prototype, the device was well tolerated by both operators and patients. The feedback was consistently positive and is provided below. Furthermore, there were no device-related adverse events reported for the patients defined in the primary study analysis.