A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.

ABSTRACT:

Background

Because chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed.

Research question

Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP?

Study design and methods

A retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP us

SUBMITTER: Chung JH 

PROVIDER: S-EPMC11026174 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature