Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach.

ABSTRACT:

Purpose

To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).

Methods

We develop a DL-based model to learn a compact representation of a subject, which is predictive of COPD physiologic severity and other outcomes. Our DL model learned: (a) to extract informative regional image features from HRCT; (b) to adaptively weight these features and form an aggregate patient representation; and finally, (c) to predict several COPD outcomes. The adaptive weights correspond to the regional lung contribution to the disease. We evaluate the model on 10 300 participants from the COPDGene cohort.

Results

Our model was strongly predictive of spiro

SUBMITTER: Singla S 

PROVIDER: S-EPMC7965349 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature