Sonde Health's Respiratory Responsive Vocal Biomarker Tool Identifies Patients with Respiratory Conditions

Sonde Health's respiratory responsive vocal biomarker (RRVB) machine learning model has been proven to be able to differentiate patients with COVID-19 from healthy individuals with about 70 percent accuracy.

The peer-reviewed study, which was published in The Journal of Medical Internet Research, suggests the RRVB tool could serve as a pre-screening tool for acute respiratory infection and pave the way for the development of voice-based tools for future disease detection and monitoring applications.

The RRVB tool had already shown strong performance in differentiating patients with asthma, chronic obstructive pulmonary disease (COPD), interstitial lung disease, and cough from healthy individuals. In the new study, conducted with Montefiore Health System, Brigham and Women's Hospital, U.C. San Diego Health System, and Deenanath Mangeshkar Hospital in India, the model achieved 73 percent sensitivity and 63 percent specificity for the entire COVID-19 population (97 patients), and it detected 66 percent of asymptomatic COVID-19 subjects (46 patients) using only a six-second recording of an aah vowel sound on patient smartphones. These findings suggest the tool could help uncover respiratory conditions before symptoms arise.

"We have shown that the same technology originally developed for asthma and COPD can be applied to pre-screen for COVID-19 with meaningful sensitivity and specificity," said Erik Larsen, senior vice president of clinical development and customer success at Sonde Health, in a statement. "This study demonstrates the robustness of our tool across conditions, geographies, and languages, paving the way for broader respiratory disease monitoring and surveillance efforts going forward."

"This study highlights the potential of vocal biomarkers to improve access and outcomes for diverse and varied populations with respiratory diseases," Dr. Sunit Jariwala, professor of medicine and director of clinical research and innovation in the Department of Medicine at Einstein College of Medicine and Montefiore Health System and principal investigator for the study, said in a statement. "By utilizing a digital tool that is non-invasive and can be easily scaled and distributed, we can effectively monitor respiratory health and identify individuals' levels of symptoms and risk. Based on the promising results from this study, we are working with Sonde Health to study the RRVB tool for respiratory monitoring in patients with moderate-to-severe asthma, and we are at the beginning stages of an Agency for Healthcare Research and Quality (AHRQ)-funded study to incorporate the RRVB tool into our own ASTHMAXcel mobile platform."

Study Confirms Voice's Ability to Detect Mental Health Issues

Study points to mental fitness vocal biomarkers (MFVB) as a promising approach to support mental health monitoring, engagement, and care.

12 Mar 2024