How AI Rewired Plaque Detection for a Leading U.S. Cardiovascular Lab
Challenge
Manual Processing Bottlenecks
Each scan required 180+ seconds of expert analysis, with increasing backlog and no scalable way to meet demand.
Inconsistent Accuracy
Inter-reader variability made plaque measurement and diagnosis inconsistent.
No Predictive Insights
The existing workflow offered no visibility into arterial aging or future cardiovascular risk.
High Per-Report Cost
Manual review models raised overhead, limited scalability, and affected operational margins.
Manual Processing Bottlenecks
Each scan required 180+ seconds of expert analysis, with increasing backlog and no scalable way to meet demand.
Inconsistent Accuracy
Inter-reader variability made plaque measurement and diagnosis inconsistent.
No Predictive Insights
The existing workflow offered no visibility into arterial aging or future cardiovascular risk.
High Per-Report Cost
Manual review models raised overhead, limited scalability, and affected operational margins.
Solution
We deployed an end-to-end AI pipeline that automatically and accurately analyzes ultrasound scans, transforming the client’s diagnostic workflow. The system begins with image preprocessing, where DICOM files are normalized and standardized for optimal model performance. A U-Net–based segmentation model then identifies arterial walls, plaque boundaries, and thickening zones with high precision. From these segmented regions, the AI engine extracts key clinical features—including CIMT, plaque thickness, stiffness, and calcification levels—to build a comprehensive arterial health profile.
A regression-based prediction module further estimates arterial age and progressive plaque burden, giving clinicians deeper insight into long-term cardiovascular risk. All results are seamlessly integrated into the lab’s existing web-based diagnostic interface, enabling fast, accurate, and clinician-ready reporting.
We deployed an end-to-end AI pipeline that automatically and accurately analyzes ultrasound scans, transforming the client’s diagnostic workflow. The system begins with image preprocessing, where DICOM files are normalized and standardized for optimal model performance. A U-Net–based segmentation model then identifies arterial walls, plaque boundaries, and thickening zones with high precision. From these segmented regions, the AI engine extracts key clinical features—including CIMT, plaque thickness, stiffness, and calcification levels—to build a comprehensive arterial health profile.
A regression-based prediction module further estimates arterial age and progressive plaque burden, giving clinicians deeper insight into long-term cardiovascular risk. All results are seamlessly integrated into the lab’s existing web-based diagnostic interface, enabling fast, accurate, and clinician-ready reporting.
Results
In cardiovascular imaging, manual reviews not only slowed operations but also introduced clinical inconsistency. Each scan took 2–3 minutes to analyze, and interpretations varied between radiologists. This affected throughput, increased cost per report, and left no room for predictive risk modeling.
Technostacks deployed an AI-powered solution that automated plaque segmentation and clinical metric extraction—delivering structured, predictive outputs in under 30 seconds per scan.
In cardiovascular imaging, manual reviews not only slowed operations but also introduced clinical inconsistency. Each scan took 2–3 minutes to analyze, and interpretations varied between radiologists. This affected throughput, increased cost per report, and left no room for predictive risk modeling.
Technostacks deployed an AI-powered solution that automated plaque segmentation and clinical metric extraction—delivering structured, predictive outputs in under 30 seconds per scan.