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Deep Learning–Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality

Authors
Hyungin Park, MD, Eui Jin Hwang, MD, PhD, and Jin Mo Goo, MD, PhD
Journal
Investigative Radiology
Related Product

LCS, COPD

Date Published
2023.07
doi
https://doi.org/10.1097/RLI.0000000000001003
Summary

This retrospective study examined the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) with deep learning-based kernel adaptation on long-term mortality. The study utilized LDCT scans from asymptomatic individuals aged 60 years or older and employed CorelineSoft's software, which incorporated a kernel adaptation algorithm, to quantify emphysema. The results showed that after kernel adaptation, higher levels of emphysema quantification were associated with an increased risk of nonaccidental death. This study highlights the effectiveness of CorelineSoft's software in accurately quantifying emphysema and its potential as a predictive tool for long-term mortality in asymptomatic individuals.

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