Morís Daniel I, de Moura Joaquim, Marcos Pedro J, Míguez Rey Enrique, Novo Jorge, Ortega Marcos
Varpa Group, Biomedical Research Institute A Coruña (INIBIC), University of A Coruña, 15006, A Coruña, Spain.
Department of Computer Science and Information Technologies, University of A Coruña, 15071, A Coruña, Spain.
Heliyon. 2024 Oct 10;10(20):e38642. doi: 10.1016/j.heliyon.2024.e38642. eCollection 2024 Oct 30.
COVID-19 is an infectious disease that caused a global pandemic in 2020. In the critical moments of this healthcare emergencies, the medical staff needs to take important decisions in a context of limited resources that must be carefully managed. To this end, the computer-aided diagnosis methods are extremely powerful and help them to better recognize the evidences of high-risk patients. This can be done with the support of relevant information extracted from electronic health records, lab tests and imaging studies. In this work, we present a novel fully-automatic efficient method to help the clinical decision-making process in the context of COVID-19 risk estimation, using multimodal data fusion of clinical features and deep features extracted from chest X-ray images. The risk estimation is studied in two of the most relevant and critical encountered scenarios: the risk of hospitalization and mortality. This study shows which are the most important features for each scenario, the ratio of clinical and imaging features present in the top ranking and the performance of the used machine learning models. The results demonstrate a great performance by the classifiers, estimating the risk of hospitalization with an AUC-ROC of 0.8452 ± 0.0133 and the risk of death with an AUC-ROC of 0.8285 ± 0.0210, only using a subset of the original features, and highlight the significant contribution of imaging features to hospitalization risk assessment, while clinical features become more crucial for mortality risk evaluation. Furthermore, multimodal data fusion can outperform the approaches that use one data source. Despite the model's complexity, it requires fewer features, an advantage in scenarios with limited computational resources. This streamlined, fully-automated method shows promising potential to improve the clinical decision-making process and better manage medical resources, not only in the context of COVID-19, but also in other clinical scenarios.
新型冠状病毒肺炎（COVID-19）是一种在2020年引发全球大流行的传染病。在这场医疗紧急情况的关键时刻，医护人员需要在必须谨慎管理的有限资源背景下做出重要决策。为此，计算机辅助诊断方法极为强大，可帮助他们更好地识别高危患者的证据。这可以借助从电子健康记录、实验室检查和影像学研究中提取的相关信息来实现。在这项工作中，我们提出了一种新颖的全自动高效方法，利用临床特征与从胸部X线图像中提取的深度特征的多模态数据融合，在COVID-19风险评估的背景下辅助临床决策过程。我们在两个最相关且关键的常见场景中研究风险评估：住院风险和死亡风险。本研究展示了每个场景中最重要的特征、排名靠前的临床和影像特征的比例以及所使用机器学习模型的性能。结果表明，分类器表现出色，仅使用原始特征的一个子集，住院风险评估的曲线下面积（AUC-ROC）为0.8452±0.0133，死亡风险评估的AUC-ROC为0.8285±0.0210，并突出了影像特征对住院风险评估的显著贡献，而临床特征在死亡风险评估中变得更为关键。此外，多模态数据融合的表现优于使用单一数据源的方法。尽管模型复杂，但它所需的特征更少，这在计算资源有限的场景中是一个优势。这种简化的全自动方法不仅在COVID-19背景下，而且在其他临床场景中，都显示出改善临床决策过程和更好管理医疗资源的广阔潜力。
