# Artificial intelligence risk stratification from dynamic digital subtraction angiography radiomics predicts pulmonary embolism and associates with clinical outcomes in deep vein thrombosis: A retrospective cohort study

## TL;DR

An AI system using dynamic angiography data improves risk prediction for pulmonary embolism in deep vein thrombosis patients and leads to better clinical outcomes.

## Contribution

A novel AI model integrating radiomic features from dynamic DSA improves risk stratification and clinical outcomes in deep vein thrombosis.

## Key findings

- The AI model outperformed the Wells score in predicting pulmonary embolism (AUC 0.88 vs 0.76).
- AI-guided therapy reduced PE incidence by 54% and severe post-thrombotic syndrome by 62%.
- AI stratification reduced inferior vena cava filter use without increasing bleeding events.

## Abstract

Current risk stratification for lower extremity deep vein thrombosis remains limited, often failing to identify high-risk patients for impending pulmonary embolism (PE) and leading to non-guideline-concordant overtreatment. We aimed to develop and validate a novel artificial intelligence (AI) system that processes dynamic digital subtraction angiography (DSA) radiomics, with the potential to guide precision therapy during endovascular intervention.

In a retrospective cohort study of 168 patients treated at a single vascular surgery center (2019-2023), we developed a hybrid deep learning model integrating a transformer-UNet for spatial feature extraction and a long short-term memory (LSTM) network for temporal hemodynamic analysis. This model processed intraprocedural dynamic DSA sequences to quantify novel thrombus kinematic parameters (eg, displacement velocity, oscillation angle θ) and hemodynamic parameters venous (quantitative flow ratio). The model's performance for predicting subsequent PE was compared against the Wells score. Its impact on clinical decision-making and 12-month outcomes was evaluated rigorously.

The AI model demonstrated significantly superior discriminative performance for predicting PE compared with the Wells score (area under the curve, 0.88; 95% confidence interval [CI], 0.85-0.92 vs 0.76; 95% CI, 0.70-0.83; P = .026). Implementation of the AI-guided strategy was associated with markedly improved clinical outcomes at the 12-month follow-up: a 54% lower incidence of PE (3.4% vs 11.1%; relative risk [RR], 0.46; 95% CI, 0.08-0.82; P = .005), a 62% lower incidence of severe post-thrombotic syndrome (Villalta score ≥10; 8.0% vs 21.0%; RR, 0.38; 95% CI, 0.17-0.86; P = .008), and a lower prevalence of preexisting inferior vena cava filters in the AI-stratified high-risk group (25.3% vs 44.4%; RR, 0.57; 95% CI, 0.36-0.89; P < .001), without a significant increase in major bleeding events (2.3% vs 7.4%; P = .096).

An AI-guided risk stratification system based on dynamic DSA radiomics accurately identifies thrombus instability and hemodynamic impairment in real time and suggests its potential to help enable more personalized therapeutic decisions during intervention. In this retrospective analysis, AI-based risk stratification was associated with a significantly lower incidence of PE and severe post-thrombotic syndrome while safely curbing the overuse of inferior vena cava filters, representing a transformative advancement in the precision management of acute deep vein thrombosis.