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AI & Research

The gold standard of audio auscultation.

Why Thinklabs?


Capture quality sounds consistently so you can use all of your samples.


Hone in on a particular sound or get the full range of audio with multiple filter options.


Record, save and share sounds of unprecedented clarity with the One. You don't want to settle for subpar data.

You're only as good as your raw data.


Leading researchers worldwide use Thinklabs technology to power their data collection and breakthroughs in a diverse range of fields. In groundbreaking research and clinical trials, over 200 published papers have incorporated the Thinklabs One digital stethoscope.


Leading researchers, start ups, health systems and corporations worldwide in medicine use Thinklabs technology for machine learning and AI algorithm development to create next generation solutions to augment diagnostics and treatments in a range of diseases.  

Use the right tool for the job.

If you’re delving into the rich field of heart sound analysis, One is the tool of choice for capturing and studying sounds.  Ongoing research that’s being conducted with the One includes high-altitude pediatric pulmonary fieldwork in Nepal, a comparison of live-versus-remote diagnosis at the University of California, Irvine; and disease investigations by the Bill and Melinda Gates Foundation.


Researchers can record sounds and save them on a mobile device or upload them to a cloud repository. Now you can even capture sounds from far-flung population.


Do serial studies, recording patient progress over time, record artificial heart valves or LVADs over time to find sound signatures for changes or impending failures. Analyze signals using Matlab or other advanced signal processing software.



One is a favorite among faculty and students at leading medical schools, such as Harvard, Johns Hopkins, and University of California Los Angeles, and the Mayo Medical School. 


Using Thinklabs One is truly transformative. It not only improves the teaching of auscultation, it also can make the teaching of patient care better, and thus improve the experience of clinical medicine.

Published Research

Contact us today if you're doing a clinical trial or are a clinical trial platform provider.


Electronic stethoscopes convert acoustic sound waves to electrical signals which can then be amplified and processed for optimal listening. However, amplification of stethoscope contact artifacts, and component cutoffs has led to the question of whether they are an improvement in the bedside cardiac examination. In this study, a single observer compared an analog stethoscope with the Thinklabsone electronic stethoscope in a clinical setting to determine if there was a significant difference in the diagnostic utility of the devices. Two hundred and nine patients were examined and the electronic stethoscope was felt to have superior sound quality in 65% of patients.


Listening to the sounds made by the lungs is a long-standing method that is still used to diagnose lung diseases. Many studies have been conducted on the automatic recognition of recorded sounds from the lungs. However, in these studies, respiratory cycles, i.e. inhalations followed by exhalations, were either monitored manually or by using multichannel signals of the sounds made by the lungs. In order to recognize sounds made by the lungs automatically, one must first use these sounds to determine the respiratory cycles...In the present study, a new method has been developed in which important changes were made to reduce or negate the effects of ambient noises. 


This research work is aimed at improving health care, reducing cost, and the occurrence of emergency hospitalization in patients with Congestive Heart Failure (CHF) by analyzing heart and lung sounds to distinguish between the compensated and decompensated states. Compensated state defines stable state of the patient but with lack of retention of fluids in lungs, whereas decompensated state leads to unstable state of the patient with lots of fluid retention in the lungs, where the patient needs medication. Acoustic signals from the heart and the lung were analyzed using wavelet transforms to measure changes in the CHF patient’s status from the decompensated to compensated and vice versa...

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