Pixel-Wise Interstitial Lung Disease Interval Change Analysis: A Quantitative Evaluation Method for Chest Radiographs Using Weakly Supervised Learning

TL;DR

This paper introduces a new method using chest X-rays and machine learning to track changes in interstitial lung disease over time.

Contribution

A weakly supervised learning framework with a novel ILD extent scoring algorithm for pixel-wise analysis of ILD progression.

Findings

The ILD extent score achieved 92.98% accuracy in distinguishing ILD from normal cases.

The method assessed disease progression with 85.29% accuracy using a follow-up dataset.

The proposed method shows potential for more reliable ILD monitoring than conventional visual assessments.

Abstract

Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD.

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