End‐to‐end domain knowledge‐assisted automatic diagnosis of idiopathic pulmonary fibrosis (IPF) using computed tomography (CT). Issue 5 (19th March 2021)

• Record Type: Journal Article
• Authors: Yu, Wenxi; Zhou, Hua; Goldin, Jonathan G.; Wong, Weng Kee; Kim, Grace Hyun J.
• Is Part Of: Medical physics. Volume 48:Issue 5(2021)
• Page Start: 2458
• Page End: 2467
• Publication Date: 2021-03-19
• Subjects: computed tomography -- deep learning -- idiopathic pulmonary fibrosis (IPF) -- optimal design; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14754
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Domain knowledge (DK) acquired from prior studies is important for medical diagnosis. This paper leverages the population‐level DK using an optimality design criterion to train a deep learning model in an end‐to‐end manner. In this study, the problem of interest is at the patient level to diagnose a subject with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using a computed tomography (CT). IPF diagnosis is a complicated process with multidisciplinary discussion with experts and is subject to interobserver variability, even for experienced radiologists. To this end, we propose a new statistical method to construct a time/memory‐efficient IPF diagnosis model using axial chest CT and DK, along with an optimality design criterion via a DK‐enhanced loss function of deep learning. Methods: Four state‐of‐the‐art two‐dimensional convolutional neural network (2D‐CNN) architectures (MobileNet, VGG16, ResNet‐50, and DenseNet‐121) and one baseline 2D‐CNN are implemented to automatically diagnose IPF among ILD patients. Axial lung CT images are retrospectively acquired from 389 IPF patients and 700 non‐IPF ILD patients in five multicenter clinical trials. To enrich the sample size and boost model performance, we sample 20 three‐slice samples (triplets) from each CT scan, where these three slices are randomly selected from the top, middle, and bottom of both lungs respectively. Model performance is evaluated using a fivefold cross‐validation, where each fold was stratified using a fixed proportion of IPF vs non‐IPF. Results: Using DK‐enhanced loss function increases the model performance of the baseline CNN model from 0.77 to 0.89 in terms of study‐wise accuracy. Four other well‐developed models reach satisfactory model performance with an overall accuracy >0.95 but the benefits brought on by the DK‐enhanced loss function is not noticeable. Conclusions: We believe this is the first attempt that (a) uses population‐level DK with an optimal design criterion to train deep learning‐based diagnostic models in an end‐to‐end manner and (b) focuses on patient‐level IPF diagnosis. Further evaluation of using population‐level DK on prospective studies is warranted and is underway.