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From 3 Minutes to 30 Seconds: How AI Rewired Plaque Detection for a Leading U.S. Cardiovascular Lab

Convert raw ultrasound scans into quantified arterial insights—automatically, accurately, and in under a minute.
No fatigue. No backlog. No bottlenecks.

Client

Leading U.S.-based cardiovascular diagnostics and research lab

Industry

Healthcare | Medical Imaging | Diagnostics

Timeline

6 weeks (from model development to deployment)

Quick Snapshot

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.

Brief

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.

Challenges

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

An end-to-end AI pipeline was deployed to analyze ultrasound scans automatically and accurately.

Image Preprocessing

Normalized and standardized DICOM scans for optimal model input.

U-Net Segmentation

AI model identified arterial walls, plaque boundaries, and thickening zones.

Clinical Feature Extraction

Computed CIMT, plaque thickness, stiffness, and calcification metrics.

Regression-Based Risk Prediction

Estimated arterial age and progressive burden based on extracted features.

Web-Based Integration

Results integrated directly into the lab’s diagnostic interface for seamless review.

Technology used

  • PyTorch + fastai for rapid deep learning model prototyping and optimization
  • nnU-Net for automated, high-accuracy medical image segmentation
  • OpenCV for image preprocessing, contour analysis, and feature extraction
  • Albumentations for advanced data augmentation, improving model generalization
  • Whisper STT for automated transcription and integration of clinician voice notes into diagnostic reports

Impact

90%

Reduction in image analysis time—from 180 seconds to under 30 seconds.

80%

Reduction in interpretation error rate through standardized segmentation.

2×

Increase in daily image review capacity with existing team.

85%

Improvement in output consistency and reporting reliability.

Conclusion

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.

Ready to do the same?

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