Transforming cardiovascular care through AI and precision medicine
SonoPlaqueTM uses deep learning to characterize medical images of atherosclerotic plaques, while also combining patient-specific information, all to better predict plaque instability.
How It Works
SonoPlaqueTM works in 3 easy steps:
Upload your ultrasound videos or images in just one-click
SonoPlaqueTM uses cross-sectional and longitudinal Doppler ultrasound images to assess stenosis, lipid core, calcification, fibrosis, and cap size.
Let the clinician and histopathologist-validated AI process your data
Our continuously improving and clinician-validated AI will process the images on our cloud server with unparalleled rapidity.
Receive a comprehensive report on plaque instability for better clinical judgement
Receive annotated images and quantitative descriptions of plaque features, dimensions and composition.
DOWNLOAD OUR 2022 WHITE PAPER
Traditional approaches that stratify patient risk for strokes only take into account a few important variables. However, each patient is unique and we now have the technology to ensure that a one-size-fits-all diagnosis is a thing of the past.
SonoPlaqueTM is Powerful
A deep learning-powered platform to provide a more accurate, accessible, and a faster approach to diagnosing atherosclerotic plaque instability using ultrasound technology
Simple User Interface
A clinician and researcher-friendly interface for easy use and navigation
A Holistic Approach
Our analysis includes ultrasound images, blood biomarkers and patient characteristics
Validated by Histology
Our image-analysis software is validated by the current gold-standard in plaque analysis: histology
A deep learning approach for accurate risk stratification, with continuous re-iterations and improvements
Quantified analysis of each plaque feature for an overall score of instability
Better Clinical Judgement
More information on plaque features and clinical characteristics means better decision making for patients
PLAKK is founded on 20+ years of research
Abundant research has shown that plaque morphology and composition are more accurate indicators of plaque instability and better predictors of clinical outcomes (i.e., stroke, heart attack) compared to artery blockage alone.
Our multidisciplinary team, made up of clinician-scientists, surgeons, cardiovascular researchers, and AI engineers, have all published work in peer-reviewed academic journals. Together, our work contributes to the importance of characterizing plaque instability, all to improve the prediction of heart attacks and strokes.
See PLAKK in the news!
The dream of being able to predict cerebrovascular accidents (CVAs) a little better could become a reality thanks to a tool based on deep learning designed by a small Montreal company, PLAKK.
Thank you to our partners and collaborators for their constant support
Let's Work Together!
Want to get involved? We’re happy to start a conversation.
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