Israeli deep learning imaging analytics company, Zebra Medical Vision has developed a new algorithm that is capable of detecting Intracranial Haemorrhages – or brain bleeds.

The algorithm recently received CE regulatory approval and is the newest algorithm to be included in Zebra-Med’s growing Deep Learning Imaging Analytics platform. The other algorithms that make up their  “All-In-One” AI1 business model automatically detect low bone mineral density, vertebral fractures, fatty liver, coronary artery calcium, emphysema and more.

The timely detection of brain bleeds is critical. Research has shown that such bleeds are missed anywhere between 12% and 51% of the time, and nearly 6 million people die every year of brain bleed related conditions. Such wide variability results in significantly reduced quality of patient care. According to Zebra-Med, their algorithm can identify such bleeds and provide a safety net for physicians in acute care settings.

“This new algorithm is an important addition to Zebra’s Analytics Engine,” said Chief of Clinical & Outreach Services at Intermountain Healthcare and a Neuro-radiologist, Dr Mike Phillips.

“The ability to alert radiologists and surgeons to the presence of brain bleeds is critical, and will bring significant benefits in patient care to healthcare organisations,” continued Dr Phillips.

Zebra-Med plans to deploy the algorithm for point of care detection and for worklist prioritisation helping physicians identify bleeds more accurately and with minimal delay.

“We’re excited to announce our first acute care algorithm with the potential to help radiologists better manage their workload, and properly prioritise urgent cases over others,” said Co-founder and CEO of Zebra-Med, Elad Benjamin.

“This helps take PACS & Worklist management systems to the next level in helping radiologists manage patient care, all in a transparent and globally affordable business model. Over the next few months we plan to release several more high impact algorithms, on our path to provide a versatile AI based automated radiology assistant,” concluded Benjamin.

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