Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI.

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.

Effect of noise sensitivity on psychophysiological response through monoscopic 360 video and stereoscopic sound environment experience: a randomized control trial.

Noise sensitivity is a crucial factor affecting subjective psychophysiological responses to the acoustic environment of various indoor and outdoor spaces. This study examines how noise sensitivity or hyperacusis affects the recovery of emotional and autonomic nervous system (ANS) responses when experiencing various monoscopic 360 video and stereoscopic sound environments (urban and natural) that represent the actual environment. A total of 60 general participants with mild depression, stress, and anxiety were examined using a survey to investigate individual characteristics, including noise sensitivity, and K-means clustering was used to classify them into sensitivity groups. Emotional and physiological responses were measured using the Korean edition of Profile of Mood States and by assessing heart rate variability, respectively. Overall, the emotional recovery effect was greater in the natural than the urban environment, and the homeostatic mechanism of the ANS was better maintained, thereby increasing stress resistance. Noise sensitivity did not have considerable effect on psychophysiological recovery in the natural environment, but had a significant effect on emotional response in the urban environment. This can be used as basic data in seeking customized emotional recovery for individuals using monoscopic 360 video and stereoscopic sound technology in the future.