Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need
arXiv:2606.25956v1 Announce Type: cross Abstract: Risk stratification for pulmonary embolism (PE) is critical for clinical decision-making. Stratification guidelines are based on patient medical records, parameters measured from computed tomography pulmonary angiography (CTPA), and blood tests. However, blood tests are often missing in routine practice. This work studies whether state-of-the-art models can accurately classify risk stratification from only medical records and biomarkers extracted from CTPA images. We benchmark different approaches to combine medical records and cardiac biomarke
The increasing availability of advanced AI models and medical imaging data allows for more sophisticated analyses of diagnostic information to improve patient outcomes.
This development demonstrates AI's growing capability to enhance medical decision-making, potentially leading to more accurate and efficient risk stratification in critical care scenarios.
Clinical risk stratification for pulmonary embolism can now be significantly augmented by AI, reducing reliance on potentially missing blood test data and leveraging existing medical records and imaging for better predictive power.
- · AI healthcare developers
- · Hospitals and clinics
- · Patients at risk of PE
- · Medical imaging companies
- · Traditional diagnostic methods reliant on complete data sets
Improved diagnosis and treatment pathways for pulmonary embolism are likely.
This could lead to a reduction in morbidity and mortality associated with pulmonary embolism, and potentially lower healthcare costs by optimizing resource allocation.
The success of this approach may accelerate the adoption of similar AI-driven diagnostic tools across various medical specialties, fundamentally altering clinical workflows and training.
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Read at arXiv cs.LG