Deep learning of lung diseases on computed tomography images
Creators: name: Li, Frank
Publisher: name: University of Iowa
Year: 2023
Subjects: Chronic obstructive pulmonary disease
Cluster analysis
Deep learning
Humidifier disinfectants
Post-COVID-19
public abstract
Computed Tomography
Computer Engineering

description: This research is a collection of three papers on the use of deep learning to detect and classify lung diseases or lung injuries using a CAE-FC model or a contrastive learning model. Due to a broad spectrum of clinical presentations for a complex lung disease, its pathogenesis remains unclear or unsettled. As a result, a general descriptive term such as chronic obstructive pulmonary disease (COPD) is still used. Identification of new classifications that reflect the phenotypes of these diseases is of great interest. In my research work, two deep learning models were developed to extract computed tomography (CT) imaging features for cluster analysis and identify subtypes of different lung malfunctions as indicated in the following three research projects. Initially, an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network was built to extract imaging features of lung tissue from two-channel images, and then a novel contrastive learning model with a lung volume transform was utilized to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The major findings and conclusions for each study are summarized below:With the first research project, two important latent traits, factor 0 (F0) and factor 4 (F4), among 541 former smokers with COPD were identified. F0 and F4 were sufficient to identify healthy non-smokers from subjects at risk and differentiate COPD patients at all levels of spirometry-staged severity. Because of their detectability of abnormal tissue patterns, F0 and F4 could potentially serve as surrogate markers for new COPD phenotypes either in detecting early abnormalities in susceptible subjects at risk or in assessing the rate of lung function decline in severe patients.
description: With the second research project, two subject-clusters, cluster 0 (C0) and cluster 5 (C5) were identified from 96 subjects who had been exposed to toxic humidifier disinfectants (HD), although their lung CT and PFT appeared normal. Subjects in C5 were considered as subjects not affected by HD. On the other hand, subjects in C0 were characterized by increased lung tissue and hypo-inflation, in comparison with subjects in C5, suggesting that C0 subjects may be at risk of HD-induced inflammation or lung fibrosis. Moreover, with CFPD analysis, C0 subjects tend to have higher airway resistance and HD aerosol particle deposition density, which may be related to the susceptibility to HD-induced lung damage.
description: With the third research project, two clinically meaningful subtypes among 140 post-COVID-19 subjects were identified. Cluster 1 (C1) and cluster 2 (C2) are characterized by subjects with air-trapping caused by small airways disease and subjects with airway-associated interstitial fibrotic-like patterns, respectively.
description: The results shown above indicate that the proposed deep learning models can facilitate the recognition and interpretation of these aforementioned subtypes and can eventually provide an effective customized guidance to patient’s healthcare. Hopefully, an automatic detection system for medical care can be developed accordingly in the near future.