Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

Abstract

Purpose: Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on high-resolution CT (HRCT). This study proposes a three-dimensional (3D)-based deep learning algorithm for classifying AE-IPF using HRCT images.

Materials and methods: A novel 3D-based deep learning algorithm, SlowFast, was developed by applying a database of 306 HRCT scans obtained from two centres. The scans were divided into four separate subsets (training set, n=105; internal validation set, n=26; temporal test set 1, n=79; and geographical test set 2, n=96). The final training data set consisted of 1050 samples with 33 600 images for algorithm training. Algorithm performance was evaluated using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve and weighted κ coefficient.

Results: The accuracy of the algorithm in classifying AE-IPF on the test sets 1 and 2 was 93.9% and 86.5%, respectively. Interobserver agreements between the algorithm and the majority opinion of the radiologists were good (κw=0.90 for test set 1 and κw=0.73 for test set 2, respectively). The ROC accuracy of the algorithm for classifying AE-IPF on the test sets 1 and 2 was 0.96 and 0.92, respectively. The algorithm performance was superior to visual analysis in accurately diagnosing radiological findings. Furthermore, the algorithm's categorisation was a significant predictor of IPF progression.

Conclusions: The deep learning algorithm provides high auxiliary diagnostic efficiency in patients with AE-IPF and may serve as a useful clinical aid for diagnosis.

Keywords: Imaging/CT MRI etc; Interstitial Fibrosis.

Figure 1

Data set flowchart of the study design. The flowchart diagram illustrates the division of the total cohort of HRCT scans into training, validation and test cohorts, followed by image segmentation and resampling. AE-IPF, acute exacerbation of idiopathic pulmonary fibrosis; IPF, idiopathic pulmonary fibrosis.

Figure 2

Flowchart of the training framework. The samples from high-resolution CT scans after segmentation were concatenated and fed to the video classification network SlowFast.

Figure 3

The architecture of deep learning algorithm. The slow (top) path uses lower frame rates to sample frames from the high-resolution CT scans. The fast (bottom) path uses higher frame rates for sampling, while including a fraction of the channels used by the slow path.

Figure 4

Classification performance of algorithm and radiologists for each HRCT scan on the test set. (A and B) ROC curves of algorithm and individual radiologists for AE-IPF in test set 1(A) and test set 2(B). (C and D) ROC curves of algorithm and individual radiologists for stable-IPF in test set 1(C) and test set 2(D). (E) Selected slices from an HRCT scan were correctly classified as AE-IPF by the algorithm but incorrectly classified by two radiologists. (F) Selected slices from an HRCT scan were correctly classified as AE-IPF by the radiologists but incorrectly classified by the algorithm. AE-IPF, acute exacerbations of idiopathic pulmonary fibrosis; AUC, area under the curve; HRCT, high-resolution CT; ROC, receiver operating characteristic.

Figure 5

Logistic regression curves of the algorithm and radiologists. (A) The logistic regression curve of the model’s categorisation and PaO₂/FiO₂ ratio. (B) The logistic regression curve of the radiologists’ categorisation and PaO₂/FiO₂ ratio.