RoadMap to FFRCT

The First Step to Coronary Artery Disease Diagnosis

The First Step to Coronary Artery Disease Diagnosis

The RoadMap™ analysis assists CT readers to accurately, efficiently, and consistently identify stenoses in the coronary arteries. This anatomic visualization helps readers evaluate coronary CT angiograms (CCTA) before determining the need for an FFRct. The RoadMap analysis easily integrates with HeartFlow’s Direct workflow.

Detect Disease With High Accuracy

The RoadMap analysis helps CT readers diagnose non-invasively and accurately compared to measured QCA angiogram data, ensuring disease is not missed.

Increase Efficiency for CT Readers

The RoadMap™ analysis delivers an anatomic visualization of all the coronary arteries. With the Direct workflow, you will have the anatomic and physiologic information you need to inform your CCTA read.

  •  Receive a first look with every CCTA for all coronary arteries.
  • Instantly spot the location and severity of stenosis with color-coded markers.
  • Efficiently access anatomic and physiologic information.
 
The RoadMap analysis is proven to reduce inter-reader variability.2 With increased consistency of reads across CT readers, the RoadMap analysis can help CT readers ensure patients receive appropriate subsequent care and confidently determine the need for an FFRct.

Integrate the RoadMap™ Analysis With Ease

  • Anatomy + physiology quickly available for all CCTA
  • Provides a preview of severity and location of modeled stenoses ≥ 30%
  • FFRct identifies physiologically significant lesions

...And Help Guide Treatment Decisions With FFRct

  •  The AHA / ACC guidelines highligh the use of FFRct in patients with 40-90% stenosis.
  • Management plans for over 66% of patients changed after physician received the FFRct analysis compared to CCTA alone.

Sources and References:

  • The RoadMap Analysis by HeartFlow
  • 2021 ACC/AHA Guidelines
  • Vcathlab Computation & Simulation Team

Also We suggest you read the AI in Cardiology from prevention to diagnosis based on AI article 

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