Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging
- Resource Type
- Conference
- Authors
- Olivier, Aurelien; Hoffmann, Clement; Mansour, Ali; Bressollette, Luc; Clement, Benoit
- Source
- 2023 IEEE Statistical Signal Processing Workshop (SSP) Statistical Signal Processing Workshop (SSP), 2023 IEEE. :423-427 Jul, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Power, Energy and Industry Applications
Signal Processing and Analysis
Deep learning
Ultrasonic imaging
Databases
Ultrasonic variables measurement
Pulmonary diseases
Imaging
Signal processing
Data fusion
Pulmonary embolism
Ultrasound imaging
- Language
- ISSN
- 2693-3551
Venous Thromboembolism (VTE) is a life-threatening disease encompassing pulmonary embolism and deep venous thrombosis (DVT). Pulmonary embolism occurs in 50% of patients with a proximal deep venous thrombosis. We aimed to predict the occurrence of a pulmonary embolism in patients with a DVT from clinical data and Ultrasound images of proximal thrombosis. To address this task, we proposed to use a Deep learning model that uses both images and 5 clinical factors as input and we aimed to measure the contributions compared to using only images. Promising results were obtained with both models compared to the state-of-art. The contribution of the clinical factors remains unclear but a gain in accuracy was observed when using smaller models.