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A prominent cardiovascular lab faced a growing backlog of ultrasound images requiring manual review to detect plaque and measure arterial thickness. By implementing a deep learning model tailored for image segmentation and regression, the lab dramatically reduced diagnostic turnaround time, increased reviewer consistency, and enabled predictive risk scoring—without expanding its clinical team.
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.
Each scan required 180+ seconds of expert analysis, with increasing backlog and no scalable way to meet demand.
Inter-reader variability made plaque measurement and diagnosis inconsistent.
The existing workflow offered no visibility into arterial aging or future cardiovascular risk.
Manual review models raised overhead, limited scalability, and affected operational margins.
An end-to-end AI pipeline was deployed to analyze ultrasound scans automatically and accurately.
Normalized and standardized DICOM scans for optimal model input.
AI model identified arterial walls, plaque boundaries, and thickening zones.
Computed CIMT, plaque thickness, stiffness, and calcification metrics.
Estimated arterial age and progressive burden based on extracted features.
Results integrated directly into the lab’s diagnostic interface for seamless review.
This U.S.-based cardiovascular lab moved from manual interpretation to intelligent automation in just six weeks—delivering measurable clinical improvements without additional headcount. With faster reads, predictive insights, and consistent outputs, the lab redefined operational efficiency in diagnostics.
Let’s discuss how AI can transform diagnostic imaging for your organization.
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Imagine cutting your lab’s turnaround time by 60%, freeing up analysts to focus on high-value work that drives measurable ROI-all without adding headcount.
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