Audium Health provides AI‑driven acoustic analysis tools that augment primary care physicians’ ability to diagnose respiratory conditions using digital stethoscope data. Its DxAssist platform delivers rapid, non‑invasive screening of lung sounds to improve diagnostic accuracy and reduce misdiagnosis. The company also offers VascularFlow, an acoustic monitoring solution for dialysis access patency, helping lower care costs and extend device lifespan.
Funding
Funding not disclosed
Founders
Product
Problem
Primary care physicians often underutilize lung auscultation, a key method for detecting lower respiratory conditions, due to limitations in training and the subtlety of lung sounds. This can lead to delayed or inaccurate diagnoses, resulting in increased healthcare costs and potentially adverse patient outcomes.
Solution
Audium Health offers DxAssist™, an acoustic AI deep learning platform that enhances the diagnostic capabilities of primary care physicians by accurately screening for lower respiratory conditions through lung auscultations. DxAssist™ leverages a digital stethoscope to capture lung sounds, which are then analyzed by advanced deep learning models to detect subtle indicators of respiratory illness. This technology enables rapid, non-invasive assessments, improving diagnostic accuracy and enabling earlier intervention. The platform aims to amplify the diagnostic power of physicians, leading to more informed decisions and better patient care.
Target Audience
The primary target audience includes primary care physicians seeking to improve their diagnostic accuracy for lower respiratory conditions, as well as healthcare providers focused on early detection and intervention.
Features
- Acoustic AI deep learning platform for analyzing lung sounds
- Digital stethoscope integration for capturing high-quality audio
- Detection of intricate lower respiratory conditions with enhanced accuracy
- Rapid and non-invasive assessment process
- Integration with VascularFlow™ for monitoring dialysis patients
- Collaboration with UCSF and Rapid Research in Diagnostics Development (R2D2) for TB Network