Computer-aided detection system for pulmonary embolism with integrated cardiac assessment based on embolic burden
- Autores: I. Luque del Toro, Manuel Masias Vega, Gemma Piella
- Localización: CASEIB 2023. Libro de Actas del XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica: Contribuyendo a la salud basada en valor / coord. por Joaquín Roca González, Dolores Ojados González, Juan Suardíaz Muro, 2023, ISBN 978-84-17853-76-1, págs. 186-189
- Idioma: inglés
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Resumen
Pulmonary embolism (PE) is a cardiovascular disease re- sulting from occlusion(s) in the pulmonary arteries. Its definitive diagnosis relies mainly on imaging, being comput- erized tomography pulmonary angiogram the gold standard. Recently, there has been increasing interest in automatiz- ing PE detection with the use of computer-aided detection systems, aiming to reduce workloads and enhance identifi- cation. Manual semiquantitative scores of embolic burden have also been proposed to assess PE severity and reinforce management. Yet, few attempts have been made to couple both. Here, we propose a deep learning-based system for PE detection, which exploits the visual explanations from the detector network to represent and quantize embolic burden. The resulting measurements of embolic burden are used to assess cardiac function, using a univariate logistic regres- sion model. Particularly, we propose to predict right-to-left ventricle diameter (RV/LV) ratio ≥1, a prognostic ...