Does a multimodal deep learning model improve the prediction of short-term mortality compared to the PESI score in patients with acute pulmonary embolism?
A multimodal deep learning model combining CT imaging and clinical data significantly outperforms the standard PESI score in predicting short-term mortality for patients with acute pulmonary embolism.
BACKGROUND: Accurate prediction of short-term mortality in patients with acute pulmonary embolism (PE) is critical for optimizing treatment strategies and improving patient outcomes. The Pulmonary Embolism Severity Index (PESI) is the current reference score used for this purpose, but it has limitations regarding predictive accuracy. Our aim was to develop a new short-term mortality prediction model for PE patients based on deep learning (DL) with multimodal data, including imaging and clinical/demographic data. METHODS AND RESULTS: We developed a novel multimodal deep learning (mmDL) model using contrast-enhanced multidetector computed tomography scans combined with clinical and demographic data to predict short-term mortality in patients with acute PE. We benchmarked various machine learning architectures, including XGBoost, convolutional neural networks (CNNs), and Transformers. Our cohort included 207 acute PE patients, of whom 53 died during their hospital stay. The mmDL model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 (P<0.001), significantly outperforming the PESI score, which had an AUC of 0.86 (P<0.001). Statistical analysis confirmed that the mmDL model was superior to PESI in predicting short-term mortality (P<0.001). CONCLUSIONS: Our proposed mmDL model predicts short-term mortality in patients with acute PE with high accuracy and significantly outperforms the current standard PESI score.
Çiçek et al. (Fri,) studied this question.