Elad Walach and Dr. Carl Aschkenasi

AI
How AI will become an integral part of standard care
The current medical landscape for artificial intelligence (AI) allows for early adopters to embrace AI innovation with ease, yet it has left the majority of medical institutions struggling to catch up. Elad Walach and Dr. Carl Aschkenasi examine what needs to happen for AI to become an integral part of the standard of care.
January 16, 2019
2016 09 22 14 54 51 601 Computer Head 400
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Two representative cases demonstrate the performance of PlaqueSegNet for coronary plaque segmentation. (A–D) Coronary CT angiography (CCTA) and intravascular US (IVUS) images in a 65-year-old male patient with chest pain for two months show plaque. (A) Cross-sectional IVUS image shows a lipid-rich plaque (hypoechoic, arrows). (B) Axial and (C) cross-sectional CCTA images show the same plaque (low-attenuation area, yellow line and arrows) in the proximal left anterior descending artery. (D) PlaqueSegNet segmentation of the noncalcified plaque (yellow shading) and coronary lumen (green shading) in the cross-sectional image. (E–G) CCTA images in a 57-year old female patient with chest pain for three months show multiple plaques. (E) Volume-rendered CCTA image of the coronary tree shows PlaqueSegNet plaque segmentation (yellow) in the right and left coronary arteries. (F, G) PlaqueSegNet segmentation delineates a mixed plaque (yellow shading) and coronary lumen (green shading) in the left main artery and proximal-to-mid left anterior descending artery.
CT
AI plus CCTA cuts plaque analysis time, predicts cardiac event risk
A deep-learning model quantifies coronary plaque volume from coronary CT angiography (CCTA) images.
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