This is our AI-powered solution for the detection of suspected pulmonary embolism (PE).
Use AI-powered alerts to identify suspected PE and right heart strain in patients.
Collaborate across multispecialty care teams to review patient status, discuss next steps, and activate higher levels of care based on acuity.
Expedite treatment decisions across the HIPAA-compliant platform, close the loop with colleagues, and provide status updates to treating centers.
Second scan to alert
RV Mean Absolute Error
LV Mean Absolute Error
Mustafa K. Real-world validation of a deep learning AI-based detection alogirhtm for suspected pulmonary embolism. Presented at: ARRS, April 2023.
Researchers found that Viz PE showed a real-world positive predictive value (PPV) of 80.4% and a negative predictive value (NPV) of 98.5% in over 1,200 retrospective chest CTs.
Automated detection may have a positive downstream effect on patient triage leading to accelerated care coordination, earlier diagnoses, timely initiation of life-saving interventions, and better patient outcomes.
Since integrating Viz.ai into our network, our team has been able to quickly assess and provide timely care to our pulmonary embolism patients. By closely monitoring our metrics and implementing best practices, we’re committed to improving our throughput and delivering exceptional care to all of our patients.
The Viz.ai automated CT scan clot detection system improves diagnostic acumen and expedites care for patients with acute pulmonary embolism. This will enable clinicians to quickly triage patients and treat them appropriately, by providing a powerful tool for early detection and risk stratification. This expedited critical decision-making will undoubtedly save lives
Peter Chang, MD
Radiologist and Director at UCI Center for AI in Diagnostic Medicine
Elias Iliadis, MD
Interventional Cardiologist Cooper University Healthcare
Kenneth Rosenfield, MD
Interventional Cardiologist Section Head of Vascular Medicine and Intervention at Massachusetts General Hospital and Co-Founder of the PERT Consortium™