Respiratory disease, the third leading cause of deaths globally, is
considered a high-priority ailment requiring significant research on
identification and treatment. Stethoscope-recorded lung sounds and artificial
intelligence-powered devices have been used to identify lung disorders and aid
specialists in making accurate diagnoses. In this study, audio-spectrogram
vision transformer (AS-ViT), a new approach for identifying abnormal
respiration sounds, was developed. The sounds of the lungs are converted into
visual representations called spectrograms using a technique called short-time
Fourier transform (STFT). These images are then analyzed using a model called
vision transformer to identify different types of respiratory sounds. The
classification was carried out using the ICBHI 2017 database, which includes
various types of lung sounds with different frequencies, noise levels, and
backgrounds. The proposed AS-ViT method was evaluated using three metrics and
achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20
split ratio in terms of unweighted average recall and overall scores
respectively for respiratory sound detection, surpassing previous
state-of-the-art results.

开发了一种名为音频 - 谱图视觉变换器的新方法，将肺部声音转化为称为谱图的可视化表示，通过使用视觉变换器模型对这些图像进行分析以识别不同类型的呼吸声，使用含有各种频率、噪声水平和背景的肺部声音的 ICBHI 2017 数据库对提出的音频 - 谱图视觉变换器方法进行了评估，并在呼吸道声音检测方面取得了 79.1% 和 59.8%（60:40 分割比）以及 86.4% 和 69.3%（80:20 分割比）的未加权平均召回率和总体分数，超过以往最先进的结果。

利用音频频谱图视觉转换器进行异常呼吸声识别

Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision  Transformer

Cough acoustics contain multitudes of vital information about
pathomorphological alterations in the respiratory system. Reliable and accurate
detection of cough events by investigating the underlying cough latent features
and disease diagnosis can play an indispensable role in revitalizing the
healthcare practices. The recent application of Artificial Intelligence (AI)
and advances of ubiquitous computing for respiratory disease prediction has
created an auspicious trend and myriad of future possibilities in the medical
domain. In particular, there is an expeditiously emerging trend of Machine
learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting
cough signatures. The enormous body of literature on cough-based AI algorithms
demonstrate that these models can play a significant role for detecting the
onset of a specific respiratory disease. However, it is pertinent to collect
the information from all relevant studies in an exhaustive manner for the
medical experts and AI scientists to analyze the decisive role of AI/ML. This
survey offers a comprehensive overview of the cough data-driven ML/DL detection
and preliminary diagnosis frameworks, along with a detailed list of significant
features. We investigate the mechanism that causes cough and the latent cough
features of the respiratory modalities. We also analyze the customized cough
monitoring application, and their AI-powered recognition algorithms. Challenges
and prospective future research directions to develop practical, robust, and
ubiquitous solutions are also discussed in detail.

对咳嗽数据驱动的机器学习 / 深度学习检测和初步诊断框架进行了综合概述，包括显著特征的详细列表，并分析了引起咳嗽的机制和呼吸模式的潜在咳嗽特征，以及定制咳嗽监测应用和其基于人工智能的识别算法。此外，还详细讨论了发展实用、强大和普遍解决方案的挑战和未来研究方向。