Department of Mechanical Engineering, Hanyang University, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea.
School of Electromechanical and Automotive Engineering, Yantai University, 30 Qingquan Road, Laishan District, Yantai 264005, China.
Sensors (Basel). 2021 Oct 23;21(21):7036. doi: 10.3390/s21217036.
Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.
肺炎是一种严重的疾病，常伴有并发症，有时可导致死亡。不幸的是，肺炎的诊断经常延迟到进行体格检查和影像学检查。使用咳嗽声进行肺炎诊断将是一种有利的非侵入性测试，可以在医院外进行。我们旨在开发一种基于人工智能（AI）的肺炎诊断算法。我们收集了 30 名成年肺炎或其他引起咳嗽疾病患者的咳嗽声。为了量化咳嗽声，我们使用响度和能量比来表示水平及其频谱变化。这两个特征用于构建诊断算法。为了评估开发算法的性能，我们通过将其与基于仅咳嗽声的肺病专家诊断进行比较来评估诊断准确性。该算法对肺炎组 70 例咳嗽声和非肺炎组 56 例咳嗽声的病例的敏感性、特异性和总准确性分别为 90.0%、78.6%和 84.9%。对于相同的病例，肺病专家正确诊断咳嗽声的准确性为 56.4%。这些发现表明，所提出的 AI 算法具有作为一种有效辅助技术来诊断成年肺炎患者的重要可靠性。
