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Applying our AI

Our platform

We deploy AI directly on our platform. This means users will automatically benefit from our continous improvements

API for analysis

We provide API and documentation for other platforms that seeks to leverage our AI development


We collaborate with researchers to ease their data analysis process. Contact us for more details on how we can help you


In order to ensure safety of our AI algorithms, we invesitagate and evaluate their performance with the data that was not used in the development process. It doesn’t mater if you are numbers-loving geek or just a curious person: below you will find some more technical papers concerning those issues.

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About Our AI

Medsensio uses Artificial Intelligence algorithms to help you spot the abnormalities in the auscultation recordings.


Tromsø 7

The initial work on our AI was started in 2016 as a collaboration between UiT The Arctic University of Norway and the population study Tromsø Study 7


ML pipeline

We developed a dedicated pipeline for machine learning in order to automate more of the procedure, helping us to speed up model improvements


New functionality

In 2020 we started development of our next algorithm, intended to provide more detailed information about lung health



We will CE certify our lung sound analysis tool as a medical device by the end of 2022

Our data

In order to develop our algorithms we use data collected in the Tromsø Study, one of the worlds most comprehensive population studies. In addition, we use  data obtained from chronic cardiopulmonary patients and Covid-19 patients in the Horizion 2020 project PyXy.AI

Analysis process

It doesn’t matter if you are a medical professional, student or patient - our algorithms need your help only in the recording collection process. The algorithm will analyse the recording and inform you whether it is of sufficient quality and whether any abnormal findings are present

Our algorithms

Our AI uses spectrograms to analyse recordings and recognize abnormal breathing. We display that same spectrogram for you, as research has shown auscultation analysis performance improves when both audio and visual data is combined

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