Turning 4GB RAM Smartphones into Offline Digital Stethoscopes. AASHA Neo runs edge-computing ML entirely on-device — zero cloud, zero hardware costs.
Over half the world's population has no access to a trained specialist. Community health workers are the front line — but they cannot auscultate.
Remote clinics have no reliable internet. Cloud-based AI diagnostics are useless where the network does not exist.
Traditional digital stethoscopes are too costly to deploy at scale in low-resource settings.
Traditional diagnostic software relies on API handshakes. AASHA Neo processes audio 100% locally.
Traditional digital stethoscopes cost $500+. AASHA Neo utilizes any $50 off-the-shelf Android phone.
Grassroots health workers use our automated triage framework to screen for Pneumonia and COPD locally.
A grassroots health worker presses an unmodified Android phone to a patient's chest. The microphone couples to the chest wall through a passive plastic cup. Within three seconds, the device returns a triage signal — no cloud, no specialist, no purchase order.
Raw audio from a cheap passive chest coupler runs through an on-device digital signal processing (DSP) pipeline. An adaptive bandpass filter isolates the 20 Hz to 2000 Hz cardiopulmonary window, wiping out environmental noise via CLAP architecture.
Achieved in our peer-reviewed image-based foundation framework, formally published in the Harvard Dataverse.
Acoustic CNN layers completely engineered to recognize biological markers — wheezing, crackles, and fluid blockages — natively on-device.
Synchronized validation pipeline expanding starting June 1st with researchers from Cornell University and IIT Patna.
Run a 10-second real-time acoustic inference pipeline using on-device INT8 quantization.