CMAAO CORONA FACTS and MYTH  Future of lung function screening

Author : Dr K Aggarwal , President CMAAO , With input from Dr Monica Vasudev

India

healthysoch

New Delhi, October 25, 2020 ;

Voice-Based Screening Predicts Lung Function

1. A preliminary study showed novel voice and breath analysis using a smartphone app to be a useful measure of lung function
2. The technique may prove to be a valuable tool for identifying and monitoring respiratory disease.
3. Automated voice and breath analysis was found to have value for predicting lung function, with an 82% accuracy for predicting patients with and without obstructive lung disease
4. The study was presented at the virtual CHEST conference, the annual meeting of the American College of Chest Physicians by Obaid Ashraf, MD, of Allegheny Health Network in Pennsylvania
5.  The ongoing, prospective, cross-sectional study included 128 initial participants (76 women and 52 men), who were recruited during appointments for regularly scheduled pulmonary function testing conducted at Allegheny General Hospital in Pittsburgh. Of the cohort, 16.4% had lung obstruction.
6.  A voice collector app, was used to collect voice data. Participants were asked to read a phonetically-balanced passage with 199 words selected to optimize findings. The sample passage was later reduced to around 50 words.
7.  Voice and breath sound samples were recorded before and after pulmonary function testing, which corresponded with pre-and post-bronchodilator samples.
8.  The researchers obtained pre- and post-pulmonary function test forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) results from each patient. Participant voice and breath audio samples were recorded on a smart tablet, using the proprietary software app. The recorded voice data were analyzed using cloud-based software. Voice audio recordings were calibrated to create a customized noise profile.
9.  The phonetically balanced reading passage was used to examine respiration, phonation, articulation, and resonance, while a long vowel word list was used to detect speaking-related dyspnea during articulation of long vowel sounds.
10. Machine learning was used to compare the voice-based screening to spirometry data.
11. The automated voice analysis delivered good diagnostic accuracy for the prediction of FEV1ant FVC
12. Obstruction classification showed an accuracy of 98%, a sensitivity of 96%

Source Reference: Ashraf O, et al “Voice-based screening and monitoring of chronic respiratory conditions” CHEST 2020.

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