Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence.
Authors
Affiliations (6)
Affiliations (6)
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.).
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.).
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Cincinnati Bronchopulmonary Dysplasia Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (E.B.H., J.C.W.).
- Department of Internal Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio (F.X.M.).
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Radiology, Cincinnati, Ohio (L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Biomedical Engineering, Cincinnati, Ohio (L.L.W., Z.I.C.).
- Center for Pulmonary Imaging Research, Division of Pulmonary Medicine, Cincinnati, Ohio (A.M.M., A.S.B., M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Department of Radiology, Cincinnati, Ohio (L.L.W., Z.I.C., J.C.W.); Department of Pediatrics, Cincinnati, Ohio (M.M.W., E.B.H., L.L.W., Z.I.C., J.C.W.); Cincinnati Bronchopulmonary Dysplasia Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (E.B.H., J.C.W.); Department of Physics, University of Cincinnati, Cincinnati, Ohio (J.C.W.). Electronic address: [email protected].
Abstract
Hyperpolarized <sup>129</sup>Xenon magnetic resonance imaging (MRI) measures the extent of lung ventilation by ventilation defect percent (VDP), but VDP alone cannot distinguish between diseases. Prior studies have reported anecdotal evidence of disease-specific defect patterns such as wedge-shaped defects in asthma and polka-dot defects in lymphangioleiomyomatosis (LAM). Neural network artificial intelligence can evaluate image shapes and textures to classify images, but this has not been attempted in xenon MRI. We hypothesized that an artificial intelligence network trained on ventilation MRI could classify diseases based on spatial patterns in lung MR images alone. Xenon MRI data in six pulmonary conditions (control, asthma, bronchiolitis obliterans syndrome, bronchopulmonary dysplasia, cystic fibrosis, LAM) were used to train convolutional neural networks. Network performance was assessed with top-1 and top-2 accuracy, recall, precision, and one-versus-all area under the curve (AUC). Gradient class-activation-mapping (Grad-CAM) was used to visualize what parts of the images were important for classification. Training/testing data were collected from 262 participants. The top performing network (VGG-16) had top-1 accuracy=56%, top-2 accuracy=78%, recall=.30, precision=.70, and AUC=.85. The network performed better on larger classes (top-1 accuracy: control=62% [n=57], CF=67% [n=85], LAM=69% [n=61]) and outperformed human observers (human top-1 accuracy=40%, network top-1 accuracy=61% on a single training fold). We developed an artificial intelligence tool that could classify disease from xenon ventilation images alone that outperformed human observers. This suggests that xenon images have additional, disease-specific information that could be useful for cases that are clinically challenging or for disease phenotyping.