Lung diseases pose a significant challenge to public health especially if not diagnosed early. Regular monitoring is required to prevent the progression of chronic diseases. Recent advancements in AI have helped improve the diagnostics of pulmonary medicine. Imagine using your voice to detect the quality of your pulmonary health. Shocking? Well, it happens. Vocal biomarkers are unique features in voice and speech patterns that may signal specific health conditions. Utilizing advanced AI techniques, their analysis offers promising possibilities for early and remote detection of lung diseases. [1,2]

These biomarkers are based on the concept that changes in the respiratory system can subtly alter vocal characteristics, making them detectable through advanced analysis. The human voice is generated by the coordinated function of the lungs, vocal cords, and articulatory structures such as the tongue and lips. When respiratory conditions like asthma, chronic obstructive pulmonary disease (COPD), or lung infections occur, they can impact airflow, muscle control,  lung capacity, and vocal cord vibration, leading to noticeable shifts in speech patterns. By analyzing these changes, vocal biomarker tests provide a non-invasive and cost-effective tool for early detection and monitoring of pulmonary health. For example, In COPD, where airflow is restricted, patients often have a softer voice, pitch changes, and a breathy tone in their speech. [3,4,5]

PinkTree Health is an innovative application focused on pulmonary health, designed to enhance the quality of life for patients with respiratory conditions. As the first and only health startup in India to introduce the Vocal Biomarker test, PinkTree Health revolutionizes respiratory diagnostics by offering a non-invasive, AI-driven solution to detect and track lung health issues, reducing reliance on expensive and time-consuming traditional tests. By lowering medical costs and enabling remote monitoring, PinkTree Health empowers patients and healthcare providers with real-time insights, ensuring timely intervention and better disease management.

References:

1. Reddy S. Explainability and artificial intelligence in medicine. The Lancet Digital Health. 2022 Apr 1;4(4):e214-5.
2. Chen Z, Liang N, Li H, Zhang H, Li H, Yan L, Hu Z, Chen Y, Zhang Y, Wang Y, Ke D. Exploring explainable AI features in the vocal biomarkers of lung disease. Computers in Biology and Medicine. 2024 Sep 1;179:108844.
3. Mohamed EE. Voice changes in patients with chronic obstructive pulmonary disease. Egyptian Journal of Chest Diseases and Tuberculosis. 2014 Jul 1;63(3):561-7.
4. Rossi A, Butorac-Petanjek B, Chilosi M, Cosio BG, Flezar M, Koulouris N, Marin J, Miculinic N, Polese G, Samaržija M, Skrgat S. Chronic obstructive pulmonary disease with mild airflow limitation: current knowledge and proposal for future research–a consensus document from six scientific societies. International journal of chronic obstructive pulmonary disease. 2017 Aug 29:2593-610.
5. Jones RL, Noble PB, Elliot JG, James AL. Airway remodelling in COPD: It’s not asthma!. Respirology. 2016 Nov;21(8):1347-56.