“TimBre” Pilot Study Conducted Using Multi-Country Training and Validation Data for Screening of Pulmonary Tuberculosis Using Cough (Acoustic Sounds), Clinical & Demographic Inputs

Abstract

TimBre from Docturnal offers screening for multiple lung diseases – Pulmonary Tuberculosis, Pneumonia, Covid19 & COPD. Detailed studies of TimBre in the past used a third-party Microphone Array that focused on a XY arrangement that provided high fidelity cough sounds with an average length of >5 seconds and demographic data such as Height, Weight, BMI [1]. In the current study, cough sounds were collected from 7 different countries (India, Vietnam, Philippines, Uganda, Tanzania, Madagascar, and South Africa) using Mobile Phones from different manufacturers & recorded solicited coughs in a clinic for a duration of 0.5 seconds. A plethora of demographic and clinical variables were provided of which a subset was used by TimBre algorithm. Most importantly, the .WAV files were recorded in a single channel at a sampling rate of 44.1kHz & 16 bits. The study details two approaches wherein the first method was to concatenate all the 0.5 second WAV files based on a timestamp provided for each StudyID in the training & scoring set while the second method involved using the 0.5 second snippets as-is in both training and validation sets without any concatenation. Both approaches used a combination of demographic, clinical and spectral variables. The first approach on the independent test set yielded a sensitivity and specificity of 68.6% and 71.7% respectively with an AUC of 0.75 while the second approach yielded a sensitivity & specificity of 75.41% and 68.30% respectively with an AUC of 0.78.

Thus, the ML model performed better in the second approach and we anticipate it to improve with additional training data.