用呼吸音识别间质性肺病，性能达84.86%准确率。
ILD-VIT: A Unified Vision Transformer Architecture for Detection of Interstitial Lung Disease from Respiratory Sounds
- 将呼吸音转为梅尔频谱图，用视觉变压器分类
- 在双数据集上实现84.86%准确率、82.67%敏感度
- 可部署于树莓派，适合基层筛查
间质性肺病（ILD）是一类导致肺部纤维化、永久性损伤的慢性限制性疾病，常通过肺功能检测、高分辨率影像和爆裂音（RSs）等手段诊断。本文提出一种基于视觉变换器（VIT）的深度学习框架ILD-VIT，利用呼吸音记录检测ILD。该框架包含预处理、梅尔频谱图提取及基于梅尔频谱图像块的VIT分类三个阶段。在公开数据集BRACETS和KAUH上的实验表明，该方法在独立受试者盲测中达到84.86%准确率、82.67%敏感度和86.91%特异性。成功在树莓派4微型控制器上部署，显示出其在真实临床环境中作为独立筛查系统的潜力。
原文摘要 · Abstract (English)
Interstitial lung disease (ILD) represents a group of restrictive chronic pulmonary diseases that impair oxygen acquisition by causing irreversible changes in the lungs such as fibrosis, scarring of parenchyma, etc. ILD conditions are often diagnosed by various clinical modalities such as spirometry, high-resolution lung imaging techniques, crackling respiratory sounds (RSs), etc. In this letter, we develop a novel vision transformer (VIT)-based deep learning framework namely, ILD-VIT, to detect the ILD condition using the RS recordings. The proposed framework comprises three major stages: pre-processing, mel spectrogram extraction, and classification using the proposed VIT architecture using the mel spectrogram image patches. Experimental results using the publicly available BRACETS and KAUH databases show that our proposed ILD-VIT achieves an accuracy, sensitivity, and specificity of 84.86%, 82.67%, and 86.91%, respectively, for subject-independent blind testing. The successful onboard implantation of the proposed framework on a Raspberry-pi-4 microcontroller indicates its potential as a standalone clinical system for ILD screening in a real clinical scenario.
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