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In Medical physics ; h5-index 59.0

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 inter-observer 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 multi-center 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 five-fold cross-validation, where each fold was stratified using a fixed proportion of IPF versus 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 greater than 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 (1) uses population-level DK with an optimal design criterion to train deep learning-based diagnostic models in an end-to-end manner and (2) focuses on patient-level IPF diagnosis. Further evaluation of using population-level DK on prospective studies is warranted and is underway.

Yu Wenxi, Zhou Hua, Goldin Jonathan G, Wong Weng Kee, Kim Grace Hyun J


computed tomography, deep learning, idiopathic pulmonary fibrosis (IPF), optimal design