Helmet Scanner Diagnoses Brain Stroke in Minutes at Patient Bedside

One in every four adults will experience a stroke at some point in their lives, making it the number one cause of both disability and death across the globe. Every untreated minute after a stroke costs the patient almost 2 million neurons, which equates to a loss that would normally occur over three and a half years of natural aging. Now, a portable imaging device makes it easier to quickly image the brain, eliminating the need to transport patients to a radiology department - a process that can be time-consuming even when the patient is already in a hospital.

EMVision Medical Devices (Sydney, Australia) has developed a mobile brain imaging device that is shaped like a helmet. Roughly the size of a standard ultrasound machine, the device is designed to be easily moved to a patient's bedside on wheels. The device enables medical images to be taken in regional hospitals and then sent to stroke specialists via telehealth for diagnosis and treatment guidance. This is particularly beneficial for patients in remote areas, far from radiology departments. Multiple research trials are ongoing, and the company plans to release the device to the market after study results are finalized in 2024-25.

Additionally, EMVision will begin trials next year for a more compact model of this device. This smaller model is intended for use in both road and air ambulances. The second-generation helmet scanner aims to fill the existing gaps in both bedside and emergency responder brain imaging. Weighing less than 22 pounds, the device can be transported to the point of care in a backpack. It features a 28-antenna 3D array that's engineered to scan the entire brain in one go. A silicone membrane with a coupling medium connects the antennas to the patient's head. These trials will examine various technical aspects against simulations and will also include tests for target detection. The objective is to gain clearance from the U.S. Food and Drug Administration for this second-generation scanner, building upon the success of the first model.

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