New clinical data support AI solution for improved PE detection and care coordination

1402 has announced new clinical data supporting advancements in pulmonary embolism (PE) detection. Two studies have demonstrated the real-world clinical efficacy of’s PE module to quickly and accurately identify PE and associated right heart strain, accelerate care coordination, and improve healthcare workflow efficiency, the company reports. 

The first study—”Automated PE clot detection and RV/LV ratio measurement using AI [artificial intelligence]-based deep learning algorithms: A preliminary validation study”—evaluated the performance of Viz PE and Viz right ventricle/left ventricle (RV/LV) algorithms. The study found that, across 100 retrospectively collected chest computed tomography pulmonary angiogram (CTPA) images, Viz PE demonstrated a sensitivity of 91.1% and specificity of 100%. Furthermore, the study revealed a significant positive correlation between algorithmic and manual calculation of RV/LV ratio.

“Our preliminary findings underscore the remarkable performance of Viz PE and Viz RV/LV,” said Parth Rali, MD, associate professor of thoracic medicine and surgery at Lewis Katz School of Medicine at Temple University in Philadelphia. “We are excited to partner with and pioneer investigator-initiated research that will reveal the impact of AI technology in revolutionizing patient care.”

The second—”The use of artificial intelligence technology in the detection and treatment of pulmonary embolism at a tertiary referral center”—demonstrated how Viz PE directly improves patient wait times for evaluation, the company commented. Adoption of’s technology significantly reduced time to consult on average from four hours to six minutes, leading to faster diagnosis and initiation of treatment. When combined with multidisciplinary evaluation by an existing pulmonary embolism response team (PERT), time to radiology report was reduced by 109 minutes, showcasing the potential combined benefits of AI technology and the PERT model of care on PE care and management.

“The integration of AI technology into our PE workflow has significantly shortened the time-to-consult, helping us to promptly evaluate and triage these potentially unstable patients,” said Jacob Shapiro, MD, a vascular surgery resident at TriHealth in Cincinnati, Ohio. “This advancement has the possibility to reshape the landscape of PE management.”


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