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

Abstract

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.

Identification of Elderly Patients with Lower Respiratory Tract Infection by Artificial Intelligence Analysis of Cough Pattern Sounds

“…In recent years, automatic analysis of cough sounds combined with personal medical data has shown great promise in the intelligent assessment of respiratory diseases [19]. Based on empirical-mode decomposition, which is a general-purpose signal processing method for acoustic and vibration data, Chung et al [14] established a diagnostic algorithm through long and short-term memory to classify cough sounds caused by pneumonia or non-pneumonia, with a diagnostic accuracy of 84.9%. Based on the application of the SVM classifier, Reynolds et al [20] under-lined that the automatic recognition of cough airflow signals has the potential in assessing the mechanical properties of the pulmonary system by identifying abnormal spirometry.…”

“…For the analysis of baseline data, we selected clinically meaningful factors from a previous study, including gender, age, body mass index (BMI) (kg/m 2 ), smoking history (never, ex-smoker or current smoker), and visual analogue scale of cough. Cough severity was measured on a visual analogue scale of 0 to 10 (highest) [14].…”

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

“…Various AI models have been used in detecting and analyzing lung sounds [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ]. These methods have been tested in and proposed to be used in a multitude of clinical settings.…”

Evaluation of smartphone-based cough data in amyotrophic lateral sclerosis as a potential predictor of functional disability

“…During the last few years, objective evaluation of cough sounds, in particular evaluating its quantitative characteristics in terms of sound frequency or intensity, has gained popularity for detecting and distinguishing different respiratory dysfunctions (12,(14)(15)(16)(17)(18). The increasing evidence concerning the objective evaluation of cough is also grounded by the physiological mechanisms of coughing which require considerable coordination and timing of breathing, thus being sensitive to abnormalities in the respiratory system (19,20).…”