Pooja Rao, Ph.D., co-founder of Qure.AI and head of research and development for the company, explains how the company's artificial intelligence (AI)-based auto detection software can be used to analyze radiology images. The vendor offers a U.S. Food and Drug Administration (FDA)-cleared emergency room computed tomography (CT) scan automated AI analysis tool to immediately identify areas of suspected intracranial bleeds and cranial fractures. The software offers immediate feedback for suspected areas of interest for the attending physician or stat read radiologist. This can enable faster diagnosis and treatment in neuro imaging cases, especially in meeting door to TPA time in patients with ischemic stroke.
Qure.AI also developed AI-based lung analysis software to detect a variety of abnormalities, which is working its way through FDA review. It is being used in some developing countries for mobile lung screening programs in remote areas. The vendor developed a self-contained unit for the AI to work without a PACS system or internet connection so there is immediate feedback on the image if someone may be positive. This greatly reduces the complexities of patient call backs in low-income areas that might be without out phones or web connectivity for followup. Rao explains how the technology is being implemented in this use case. AI might have its greatest impact on developing countries that do not have adequate healthcare resources of doctors.
qER detects and prioritizes scans containing Intracranial bleeds, cranial fractures, mass effect and midline shift. Image markings, bleed subtypes and labels are not available in the United States.