Samay

The sensing method has no non AI interpretation path. The device emits low frequency sound into the chest and reads returning tissue resonance, and machine learning converts that acoustic signal into lung structure and function measures including air trapping, lung volume, capacity, and flow rates. Unlike spirometry, which produces a directly interpretable measurement, acoustic resonance is meaningless without the model, so the algorithm is the instrument.

Output is presented for clinician assessment, with results surfaced through a companion app and a research portal for pulmonologists and primary care physicians to review, and the stated intent is to let clinicians intervene early rather than to automate a decision. Because the platform is investigational rather than cleared, the practical oversight position today is that all output sits in a research or monitoring context and cannot be relied on diagnostically. No published detail describes alerting thresholds or escalation logic for detected deterioration.

The physical method is described in unusual detail, covering active acoustic resonance, the patented approach of projecting sound and reading returning signal, and the pairing of lung physiology with IoT sensors, digital signal processing, and machine learning. The company has also presented method and results in a respiratory medicine conference abstract. The model itself is not described, and no algorithm documentation or validation methodology beyond headline accuracy figures was located.

Stronger evidence than most companies at this stage, and appropriately caveated. A 110 patient study conducted with a Florida pulmonary institute across COPD, asthma, and healthy controls reported detection of air trapping at 83 percent accuracy against hospital pulmonary function tests and COPD diagnosis at 90 percent accuracy, with statistically significant associations in medication efficacy pre and post bronchodilation. Results were presented as an abstract in the American Journal of Respiratory and Critical Care Medicine, and the company received NIH small business program support. Buyers should weigh three limits: cohort size is modest, the abstract is not a full peer reviewed paper, and the study used a prototype device.

No published data protection or PHI framework was located. The platform captures continuous physiological data from a body worn device paired to a patient's phone, which is an unusually persistent data stream, and no retention, encryption, or consent terms are disclosed.

No HIPAA or BAA commitment located. The company's current engagements appear to be research studies and pharmaceutical trial endpoint capture rather than provider deployments, contexts governed by trial protocol and IRB rather than a vendor BAA, but a health system considering monitoring use would need this established.

No SOC 2, ISO 27001, or equivalent attestation was located, consistent with the company's stage.

No FDA clearance is evidenced and the device is described in prototype and development terms across sources, including a peer reviewed abstract stating the company is developing the wearable to facilitate COPD diagnosis. This is the central constraint on the product: a device measuring lung function and diagnosing COPD is squarely within device regulation, so absent clearance its use is confined to research, clinical trial endpoint capture, and investigational monitoring. NIH small business program funding is research support rather than any form of regulatory standing. Buyers should treat every published accuracy figure as investigational.

No governance framework or bias evaluation was located. The relevant question for an acoustic sensing device is whether signal quality and model accuracy vary with body habitus, chest wall thickness, sex, and comorbidity, since resonance depends on tissue properties; nothing published addresses that.

No EHR integration is evidenced. The stack today is device plus mobile app plus a research portal, which is a study oriented architecture rather than a clinical workflow one.

No hosting or data residency disclosure was located. The deployment surface is a chest worn device paired wirelessly to a patient's phone or tablet with data surfaced through a portal.

No published pricing. Third party commentary has framed the device against the cost of CT scanners and formal pulmonary function testing, and the company positions three commercial contexts, primary care early detection, home monitoring, and pharmaceutical trial endpoint capture, but no rates or structures are disclosed.

Narrow by design and clearly stated: respiratory, specifically COPD and small airway disease, with studied populations spanning COPD, asthma, and healthy controls. Three intended settings are named, primary care early detection, continuous home monitoring, and high frequency endpoint capture in pharmaceutical clinical trials. The trial endpoint use case is the one least constrained by the absent FDA clearance and is therefore the nearest term commercial path.