Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images

Abstract

Objectives: Predicting intensive care unit (ICU) admissions during pandemic outbreaks such as COVID-19 can assist clinicians in early intervention and the better allocation of medical resources. Artificial intelligence (AI) tools are promising for this task, but their development can be hindered by the limited availability of training data. This study aims to explore model development strategies in data-limited scenarios, specifically in detecting the need for ICU admission using chest X-rays of COVID-19 patients by leveraging transfer learning and data extension to improve model performance. Methods: We explored convolutional neural networks (CNNs) pre-trained on either natural images or chest X-rays, fine-tuning them on a relatively limited dataset (COVID-19-NY-SBU, n = 899) of lung-segmented X-ray images for ICU admission classification. To further address data scarcity, we introduced a dataset extension strategy that integrates an additional dataset (MIDRC-RICORD-1c, n = 417) with different but clinically relevant labels. Results: The TorchX-SBU-RSNA and ELIXR-SBU-RSNA models, leveraging X-ray-pre-trained models with our training data extension approach, enhanced ICU admission classification performance from a baseline AUC of 0.66 (56% sensitivity and 68% specificity) to AUCs of 0.77-0.78 (58-62% sensitivity and 78-80% specificity). The gradient-weighted class activation mapping (Grad-CAM) analysis demonstrated that the TorchX-SBU-RSNA model focused more precisely on the relevant lung regions and reduced the distractions from non-relevant areas compared to the natural image-pre-trained model without data expansion. Conclusions: This study demonstrates the benefits of medical image-specific pre-training and strategic dataset expansion in enhancing the model performance of imaging AI models. Moreover, this approach demonstrates the potential of using diverse but limited data sources to alleviate the limitations of model development for medical imaging AI. The developed AI models and training strategies may facilitate more effective and efficient patient management and resource allocation in future outbreaks of infectious respiratory diseases.

Keywords: COVID-19; DenseNet121; Grad-CAM; ICU admission; chest X-ray; dataset expansion; deep learning; medical image analysis; transfer learning.

Figure 1

Data exclusion flowchart, depicting the selection process for the COVID-19-NY-SBU and MIDRC-RICORD-1c datasets.

Figure 2

Imaging preprocessing steps applied to sample chest X-rays, including lung segmentation (red outline), bounding box detection, and contrast enhancement using CLAHE.

Figure 3

An illustration of the model architecture of transfer learning with pre-trained DenseNet121.

Figure 4

Learning curves of the best ImageN-SBU and TorchX-SBU model. For the ImageN-SBU model, the training accuracy stabilizes at a high level, while the validation accuracy plateaus at a lower level, suggesting potential overfitting. On the other hand, the TorchX-SBU model displays a closer alignment between training and validation accuracy, indicating a more generalized model performance.

Figure 5

Grad-CAM heatmaps of the best ImageN-SBU, TorchX-SBU, and TorchX-SBU-RSNA models, demonstrating the focal areas significant for ICU admission classification. Each row represents processed X-ray images from non-ICU and ICU patients, respectively. The TorchX-SBU-RSNA model, enhanced with an extended dataset, shows a more targeted activation within the lung areas, minimizing attention to non-relevant regions like the neck, diaphragm, or shoulders, which are more pronounced in the other models. In the heatmaps, red regions indicate high importance for the model’s decision, while dark blue regions indicate low importance.