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Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based
Aissam Djahnine1, Carole Lazarus2, Mathieu Lederlin3
1Philips Research France, 92150 Suresnes, France; CREATIS, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France.
Diagnostic and Interventional Imaging
|January 23, 2024
Summary
This study introduces an AI tool for detecting pulmonary embolism (PE) and assessing its severity using 3D CTPA scans. The deep learning approach accurately identifies blood clots and quantifies severity with promising results for clinical use.
Area of Science:
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Cardiovascular Imaging
Background:
- Pulmonary embolism (PE) diagnosis relies on computed tomography pulmonary angiography (CTPA).
- Accurate quantification of PE severity is crucial for patient management.
- Current methods may require extensive manual annotation, increasing workload.
Purpose of the Study:
- To develop and evaluate a deep learning (DL) model for detecting pulmonary embolism (PE).
- To quantify PE severity using the Qanadli score and right-to-left ventricle (RV/LV) diameter ratio.
- To utilize limited annotations on 3D CTPA scans for model training.
Main Methods:
- A DL pipeline was developed for blood clot detection, PE classification, Qanadli score estimation, and RV/LV ratio prediction.
- The model was trained on a large dataset of 3D CTPA examinations with image-level annotations.
- Performance was validated on independent datasets using Area Under the Curve (AUC) for classification and R² for quantification.
Main Results:
- The DL model achieved an AUC of 0.852 on the test set for PE classification.
- The model demonstrated strong performance in estimating PE severity, with R² values of 0.717 for the Qanadli score and 0.723 for the RV/LV ratio.
- These results indicate the model's capability in both detecting PE and quantifying its severity.
Conclusions:
- Deep learning-based AI tools can effectively detect pulmonary embolism and estimate its severity from 3D CTPA.
- The proposed method leverages automated blood clot and cardiac segmentation for analysis.
- Further clinical validation is recommended to assess the real-world impact of these AI tools.
