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AI-Based CT Imaging Software Offers Paradigm Shift for CVD Screening

By MedImaging International staff writers
Posted on 22 Jul 2022
Image: ElucidVivo diagnostic imaging software is propelling the fight against CVD (Photo courtesy of Elucid)
Image: ElucidVivo diagnostic imaging software is propelling the fight against CVD (Photo courtesy of Elucid)

Myocardial ischemia occurs when blood flow to the heart is reduced, preventing the heart muscle from receiving enough oxygen. This is often the result of plaque buildup inside the artery wall. Heart attack and stroke are primarily caused by non-obstructive, but unstable plaque in the arteries that goes undiagnosed and untreated. Current non-invasive testing cannot visualize the biology deep inside artery walls where heart disease develops. Now, the only histologically-validated, objective and quantitative CTA-based arterial analysis software helps deliver personalized patient care by utilizing ground-truth histology to inform on heart attack and stroke risk.

ElucidVivo from Elucid (Boston, MA, USA) is a breakthrough FDA-cleared diagnostic imaging software that is propelling the fight against cardiovascular diseases (CVD). Using a combination of powerful image restoration and AI-driven modeling trained on pathology, it enables the identification of risk factors in CVD such as those tissues undetectable by other methods, including lipid-rich necrotic core. Non-invasive CTA, plus ElucidVivo can reduce risks and improve diagnosis for millions suffering from heart disease.

ElucidVivo is the first and only non-invasive image analysis software that employs CT angiography and patented computer algorithms, based on expert histologic examination of thousands of tissue samples, to objectively quantify the extent and stability of arterial plaque for tailored patient care. It helps assess plaque composition with histology-validated software, and quantify heart attack and stroke risk with greater accuracy. ElucidVivo helps visualize a comprehensive and objective view of arterial disease to enable personalized treatment plans before a patient ever enters the hospital.

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Image: Researchers develop a vision-language model trained on large-scale data to generate clinically relevant findings from chest computed tomography images through visual question answering (Ms. Maiko Nagao from Meijo University, Japan)

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