News | Clinical Decision Support | December 14, 2015

Machine-learning-powered workflow analyzes CT images in real time to prioritize patient care

December 14, 2015 — vRad (Virtual Radiologic) successfully reached a critical milestone in its “Deep Learning” artificial intelligence (AI) initiative, developing an algorithm that reviews computed tomography (CT) images in real time and identifies potential intracranial hemorrhaging (IH). After securing necessary regulatory approvals, the next step will be to implement this process into vRad’s patented telemedicine workflow immediately so radiologists will have the ability to optimize diagnostic review time for these life-threatening abnormalities.

IH requires immediate medical treatment, otherwise it quickly leads to increased pressure in the brain, and potentially damaged brain tissue or death. vRad will commence the process to obtain regulatory approval before the end of 2015.

vRad’s physicians used anonymized data from the company’s extensive clinical database to train AI software to search, analyze and correctly recall positive cases of IH. vRad interprets approximately 90,000 head CTs monthly, thousands of which are diagnosed with IH. With the deep-learning-powered workflow, all potential IH cases recalled by the algorithm will be “flagged” so the patient’s study can be automatically prioritized within the radiologist’s reading queue. vRad can then assign cases with potential IH to the most appropriately trained/experienced radiologist (e.g., a neuroradiologist), so they can direct their attention to the image, diagnose the condition and relay critical findings to the attending physician as quickly as possible.

Adding this capability to other priority-based workflows, including the practice’s Trauma Protocol, will allow vRad to target radiologists’ “eyes on the images” in less than the current average of four minutes after receipt from a client’s referring facility. Based on most recent test results for recall and precision, vRad expects that over 5,000 could be identified for potential IH from the IH workflow in 2016, creating faster delivery of care to those patients.

For more information: www.vrad.com

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