A Patient-Specific Pulmonary Arterial Tree Digital Twin to Extract Pulmonary Embolism Biomarkers

2026-05-27

AI Summary

This study addresses the limitations of current clinical risk stratification for pulmonary embolism, which relies on time-consuming manual scoring systems and sparsely available biomarkers, hindering rapid and precise assessment of thrombus burden and distribution in emergency settings. The authors propose a fully automated framework that constructs anatomically accurate, patient-specific digital twins of the pulmonary arteries. By leveraging artificial intelligence to generate masks of vasculature, thrombi, and lung lobes, the method integrates graph representation learning with morphological analysis to extract both local artery-level and global patient-level imaging biomarkers without human intervention. The system simultaneously outputs standardized severity scores (Qanadli and Mastora) and demonstrates high concordance with expert manual assessments, offering an efficient and accurate tool for quantifying thrombus load and spatial distribution.

Abstract

Pulmonary embolism, the obstruction of a pulmonary artery by a blood clot, is one of the leading causes of acute cardiovascular syndrome. In clinical practice, therapeutic decisions after diagnosis via computed tomography pulmonary angiography rely on risk stratification, which categorizes 30-day mortality risk into three categories. This stratification depends on the right-to-left ventricular diameter ratio and blood levels of two cardiac enzymes. However, blood biomarkers are not always available in emergency settings, and manual calculation of established severity scores - such as Qanadli and Mastora - is time-consuming and rarely performed in clinical routine practice. This study introduces an automated pipeline that models a directed graph representation of the pulmonary arterial tree, labeling its hierarchical structure and characterizing pulmonary embolism. The pipeline derives image-based biomarkers, including local artery-level features (morphological information, hierarchical position, clot volume, and resulting obstruction) and global patient-level biomarkers such as automatically calculated severity scores (Qanadli and Mastora) and the total embolic volume distribution by lobes and hierarchical levels. Using artificial-intelligence-generated binary masks of arteries, emboli, lungs, and lobes, it creates a patient digital twin of the arterial structure. Validation of the pipeline through comparison to an existing pipeline, anatomical expectations, and manual severity score calculations demonstrates the pipeline's ability to automatically generate anatomically accurate digital twins and severity scores with strong agreement. This supports the potential of these image-derived biomarkers to automatically provide rapid, precise information on thrombotic burden and spatial clot distribution.

Problem

• pulmonary embolism
• risk stratification
• biomarkers
• severity scores
• digital twin

Innovation

• digital twin
• pulmonary embolism
• image-based biomarkers
• automated severity scoring
• arterial tree modeling