Clinical validation of a deep learning algorithm for quantification of the idiopathic pulmonary fibrosis pattern

Radiologists are currently ill equipped to precisely estimate disease burden and track the progression of idiopathic pulmonary fibrosis (IPF). Development of an automated method for IPF segmentation is challenging, due to the complexity of the fibrosis pattern and degree of variation between patients. Deep neural networks are machine learning algorithms that overcome these challenges. We describe the development and validation of a novel deep learning method to quantify the IPF pattern.

We used high-resolution chest CT scans from 23 patients with IPF as training data. The fibrosis pattern was marked out on 60 slices per scan. Annotated scans, with 6 additional normal scans were used to train a convolutional neural network to outline the IPF disease pattern. Segmentation accuracy was measured using Dice score. For each patient, percentage of lungs affected by IPF was calculated. An independent set of 50 scans was used for clinical validation. Disease volume was independently estimated by 2 thoracic radiologists blinded to the algorithm estimate. Algorithm-derived estimates were correlated with radiologist estimates of disease volume.

A 3-dimensional neural network architecture coupled with 2-dimensional post-processing of each slice produced the most accurate segmentation, with a Dice score of 0.77. The correlation between algorithm-derived disease volume estimate and average radiologist estimates was 0.92. Inter-radiologist correlation was 0.89. Radiologist estimates of disease volume varied by 5.5% (range 0-15%).

We demonstrate that a deep neural network, trained using expert-annotated images, can accurately quantify the percentage of lung volume affected by IPF.