Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers

Abstract

A major obstacle when developing convolutional neural networks (CNNs) for medical imaging is the acquisition of training labels: Most current approaches rely on manual class labels from physicians, which may be challenging to obtain. Clinical biomarkers, often measured alongside medical images and used in diagnostic workup, may provide a rich set of data that can be collected retrospectively and utilized to train diagnostic models. In this work, we focused on assessing the potential of blood serum biomarkers, B-type natriuretic peptide (BNP) and NT-pro B-type natriuretic peptide (BNPP), indicative of acute heart failure (HF) and cardiogenic pulmonary edema to be used as continuously valued labels for training a radiographic deep learning algorithm. For this purpose, a CNN was trained using 27748 radiographs to automatically infer BNP and BNPP, and achieved strong performance (AUC =0.903, sensitivity =0.926, specificity =0.857, $r=0.787$ ). Also, the trained models achieved strong performance (AUC =0.801) for pulmonary edema detection when evaluated with radiologist labels. Since relevant radiographic features visible to the CNN may vary greatly based on image resolution, we also assessed the impact of image resolution on model learning and performance, comparing CNNs trained at five image sizes ( $64\times 64$ to $1024\times 1024$ ). Increasing image resolutions had diminishing but positive gains in AUC. Perhaps more importantly, experiments using three activation mapping techniques (saliency, Grad-CAM, XRAI) revealed considerably increased attention in the lungs with larger image sizes. This result emphasizes the need to utilize radiographs near native resolution for optimal CNN performance, which may not be fully captured by summary metrics like AUC.

Keywords

• Explainability
• pulmonary edema
• radiograph
• resolution
• visualization