StethoMe® – lungs under control

When can StethoMe^® help you?

Low immunity to infections
Limited access to a doctor
Verifying the first symptoms of respiratory diseases
Monitoring asthma and allergies
Managing post-COVID-19 symptoms

One device for all family members
StethoMe gives you the ability to monitor the lung health of EVERY member of your family without the need to buy separate devices.

Mobility
The device is ready to operate anywhere in the world at any time, which makes it perfect for both long and short trips.

Stop worrying, take action.
If StethoMe detects any abnormal sounds, you can just share your examination results and consult your doctor remotely. This way, the whole family gains more time to enjoy the trip together.

Low immunity to infections
Limited access to a doctor
Verifying the first symptoms of respiratory diseases
Monitoring asthma and allergies
Managing post-COVID-19 symptoms

One device for all family members
StethoMe gives you the ability to monitor the lung health of EVERY member of your family without the need to buy separate devices.

Mobility
The device is ready to operate anywhere in the world at any time, which makes it perfect for both long and short trips.

Don't let infections or asthma catch you off guard.

StethoMe^® is incredibly sensitive and detects wheezing, previously audible only to a doctor with a stethoscope. Wheezing that occurs despite anti-inflammatory treatment may signal the need for a change in dosage.

Auscultate your lungs every few days for 30 seconds. If you detect quiet wheezing, consult your doctor to react appropriately.

If asthmatic, don't wait for coughing, shortness of breath, or wheezing to appear. Once the symptoms worsen significantly, it is harder to control the acute episode and you may require bronchodilator medications.

Act ahead.

StethoMe is recommended by the Pediatric Section of the Polish Society of Allergology and the Polish Society of Pediatric Pneumonology for use for asthma in children and to assist in telemedicine visits.

Auscultate your lungs every few days for 30 seconds. If you detect quiet wheezing, consult your doctor to react appropriately.

Act ahead.

Have your complete medical history always at hand

Medical History

You'll find all examinations along with information about medications and symptoms in the app. If necessary, share them with your doctor. The more they know, the more effectively they can provide treatment.

Sharing Results

Wherever you are, you can text or email a link to the recording and examination results page. To read them, a doctor doesn't need to have StethoMe® or be registered on our platform.

How StethoMe^® works

The system detects abnormal breath sounds and measures other parameters, crucial in respiratory tract infections and managing chronic conditions such as asthma.

Thanks to StethoMe, it's possible to detect and measure the intensity of:

Wheezes and rhonchi:
These sounds often accompany exacerbations of bronchial asthma, bronchiolitis, and bronchitis, as well as in upper respiratory tract congestion.

Fine and coarse crackles:
These sounds are frequently associated with pneumonia, bronchitis, bronchiolitis, and bronchial congestion.

StethoMe AI also performs measurements of important physiological parameters:

respiratory rate,
heart rate,
inspiration/expiration ratio.

Download the StethoMe^® app to guide you through the check-up.

Simply place the device on the chest as indicated in the app.

Immediately receive your results and share them with your doctor.

StethoMe will inform you immediately if any of the parameters analyzed are abnormal.

I've been using StethoMe for 2 years now. During this time, we suspected my wife had pneumonia. The stethoscope showed that her lungs were clear, which gave the doctor grounds for a different treatment - she was prescribed a different antibiotic. Our son also has asthma, so during certain infections, we check whether his cough is an exacerbation or just another preschool infection. StethoMe also came in handy during our vacation in Thailand. We didn't have to use it, but its mere presence gave us some sense of relief that we wouldn't have to visit an unknown doctor every time someone coughed. I don't know how one can live without StethoMe.

Radosław

I have had StethoMe for a month and I regret that it came into my hands so late. The app is very easy to use and at the same time has many functions, such as examinations with AI analysis, and even the ability to send the recording of the examination to the doctor - really great! In my opinion, every parent of an asthmatic child should have this device in their home: it gives the opportunity to react in time, whether it's a weekend or a holiday, or during vacations when it is not possible to see a doctor. If I could, I would have given an extra star for customer service. The gentlemen are very nice and helpful, I got an express answer to every question. StethoMe is worth all the money. A parent's peace of mind when it comes to the health of their child is priceless. I recommend it!

Agnieszka

I have been using the stethoscope for 3 months and it is a technological miracle and something all asthmatics wish for. It allows you to perform a reliable lung examination and to fully control your asthma treatment in the comfort of your home. Moreover, full cooperation of the doctor and the peace of mind of a parent of a child suffering from asthma is invaluable. I am delighted with the stethoscope, it made the process of treatment and medical diagnosis way easier for us. Certainly, it should be named "a technological miracle for asthmatics". Thank you on behalf of myself and my son.

Bernadeta

Great device. Intuitive and very easy to use. The instructions on how to use it are displayed on the phone. When asthma symptoms get worse, this stethoscope can help you calm down your fears or take action. It took a few minutes to set up and pair with the phone. I recommend it to all parents of children with asthma.

Jadwiga

It is something I have been looking for since my first child was born, but actually I had only a thermometer. It was terrible not to have any reliable device to monitor the condition of my children when they were ill. When I look back now, the time I spent waiting for the visit when one of my children was coughing and couldn’t breathe correctly and I wasn’t sure what was going on was so nervous and cost me a lot of my own health.

Karolina Jabłońska

Imagine a physician who has gathered the experience from performing and interpreting more than 42,000 respiratory auscultations in children. Someone who is never tired and always available; never in a bad mood and with excellent hearing.

Introducing StethoMe^®. Home medical stethoscope, which is the result of over 8 years of work by 60 experts and scientists.

StethoMe^® has received a Recommendation from the Pediatric Section of the Polish Society of Allergology and the Polish Society of Pediatric Pulmonology for use in pediatric asthma and as an extension of telemedicine advice.

Our knowledge and experience

1,5 mln+

auscultatory assessments

years of research

42 tys. +

medical opinions

60+

world-class experts

Osłuchiwanie pozostaje jedną z najważniejszych metod oceny układu oddechowego ze względu na dostępność, bezpieczeństwo i niskie koszty. Badania prowadzone przez StethoMe^® w porozumieniu z Uniwersytetem Medycznym w Poznaniu służą obiektywizacji tej metody, w której niezwykle przydatna powinna być analiza akustyczna dźwięków oraz możliwość ich archiwizacji.

Prof. dr hab. Anna Bręborowicz Kierownik Kliniki Pneumonologii, Alergologii Dziecięcej i Immunologii Klinicznej Uniwersytetu Medycznego im. K. Marcinkowskiego w Poznaniu

StethoMe jest rozwiązaniem, które może pomóc w szybkim i wczesnym badaniu przesiewowym pacjentów z chorobami płuc w domu.

Prof. dr Catherine Van Der Straeten MD, PhD, FIOR Head of the Health Innovation and Research Institute Ghent University Hospital, Ghent, Belgium
President of the Council of Sciensano, The Belgian Institute for Health

Jest to urządzenie, które może bardzo pomóc także w jednym z problemów, jakie mają na co dzień liczni alergolodzy. Terminy wizyt u alergologa są zwykle wielotygodniowe, czasem wielomiesięczne. Jest mała szansa, aby pacjent w czasie wizyty był w trakcie zaostrzenia astmy (ogólnie obturacji oskrzeli). Musimy polegać na relacji pacjenta, jego rodziców. Szczególnie wątpliwe są relacje rodziców dotyczące obturacji oskrzeli u małych dzieci w trakcie infekcji układu oddechowego. Prowadzi to albo do niedoleczenia lub coraz częściej do nadrozpoznawalności astmy.

Dr n. med. Wojciech Janowicz Alergolog

Zespół StethoMe^®, fachowcy w swojej dziedzinie, stworzyli z wielką skrupulatnością i pasją urządzenie mogące ułatwić życie rodzicom chorujących dzieci. Zabrali się do tego profesjonalnie, dopracowując każdy szczegół i… udało się - pozornie małe urządzenie z dużymi możliwościami.

Paweł Małecki, M.D. Lekarz Pediatra

Poznan University of Medical Sciences

Department of Acoustics, Adam Mickiewicz University, Poznań

Poznań University of Technology

Karol Jonscher Clinical Hospital

PZU Zdrowie

Polish Ministry of Health

Frontiers in Physiology

Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients

Background

Effective and reliable monitoring of asthma at home is a relevant factor that may reduce the need to consult a doctor in person.

Aim

We analyzed the possibility to determine intensities of pathological breath phenomena based on artificial intelligence (AI) analysis of sounds recorded during standard stethoscope auscultation.

Methods

The evaluation set comprising 1,043 auscultation examinations (9,319 recordings) was collected from 899 patients. Examinations were assigned to one of four groups: asthma with and without abnormal sounds (AA and AN, respectively), no-asthma with and without abnormal sounds (NA and NN, respectively). Presence of abnormal sounds was evaluated by a panel of 3 physicians that were blinded to the AI predictions. AI was trained on an independent set of 9,847 recordings to determine intensity scores (indexes) of wheezes, rhonchi, fine and coarse crackles and their combinations: continuous phenomena (wheezes + rhonchi) and all phenomena. The pair-comparison of groups of examinations based on Area Under ROC-Curve (AUC) was used to evaluate the performance of each index in discrimination between groups.

Results

Best performance in separation between AA and AN was observed with Continuous Phenomena Index (AUC 0.94) while for NN and NA. All Phenomena Index (AUC 0.91) showed the best performance. AA showed slightly higher prevalence of wheezes compared to NA.

Conclusions

The results showed a high efficiency of the AI to discriminate between the asthma patients with normal and abnormal sounds, thus this approach has a great potential and can be used to monitor asthma symptoms at home.

Hafke-Dys H, Kuźnar-Kamińska B, Grzywalski T, Maciaszek A, Szarzyński K, Kociński J.

PLoS ONE

The accuracy of lung auscultation in the practice of physicians and medical students

Background

Auscultation is one of the first examinations that a patient is subjected to in a GP’s office, especially in relation to diseases of the respiratory system. However it is a highly subjective process and depends on the physician’s ability to interpret the sounds as determined by his/ her psychoacoustical characteristics.
Here, we present a cross-sectional assessment of the skills of physicians of different specializations and medical students in the classification of respiratory sounds in children.

Methods and findings

185 participants representing different medical specializations took part in the experiment. The experiment comprised 24 respiratory system auscultation sounds. The participants were tasked with listening to, and matching the sounds with provided descriptions of specific sound classes. The results revealed difficulties in both the recognition and description of respiratory sounds. The pulmonologist group was found to perform significantly better than other groups in terms of number of correct answers. We also found that performance significantly improved when similar sound classes were grouped together into wider, more general classes.

Conclusions

These results confirm that ambiguous identification and interpretation of sounds in auscultation is a generic issue which should not be neglected as it can potentially lead to inaccurate diagnosis and mistreatment. Our results lend further support to the already widespread acknowledgment of the need to standardize the nomenclature of auscultation sounds (according to European Respiratory Society, International Lung Sounds Association and American Thoracic Society). In particular, our findings point towards important educational challenges in both theory (nomenclature) and practice (training).

Honorata Hafke-Dys, Anna Bręborowicz, Paweł Kleka, Jędrzej Kociński, Adam Biniakowski

European Journal of Pediatrics

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination

Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. This paper investigates a new method of automatic sound analysis based on neural networks (NNs), which has been implemented in a system that uses an electronic stethoscope for capturing respiratory sounds. It allows the detection of auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles. In the blind test, a group of 522 auscultatory sounds from 50 pediatric patients were presented, and the results provided by a group of doctors and an artificial intelligence (AI) algorithm developed by the authors were compared. The gathered data show that machine learning (ML)–based analysis is more efficient in detecting all four types of phenomena, which is reflected in high values of recall (also called as sensitivity) and F1-score.

Conclusions: The obtained results suggest that the implementation of automatic sound analysis based on NNs can significantly improve the efficiency of this form of examination, leading to a minimization of the number of errors made in the interpretation of auscultation sounds.

Tomasz Grzywalski, Mateusz Piecuch, Marcin Szajek, Anna Bręborowicz, Honorata Hafke-Dys, Jędrzej Kociński, Anna Pastusiak, Riccardo Belluzzo

ERS International Congress

Respiratory system auscultation using machine learning - a big step towards objectivisation?

A stethoscope, introduced more than two centuries ago, is still a tool providing potentially valuable information gained during one of the most common examinations. However, the biggest drawback of auscultation is its subjectivity. It depends mainly on the experience and ability of the doctor to perceive and distinguish pathological signals. Many research has shown very low efficiency of doctors in this area.

Moreover, most of physicians are aware of this problem and needs supporting device. Therefore we have developed the Artificial Intelligence (AI) algorithms which recognise pathological sounds (wheezes, rhonchi, fine and coarse crackles). Here we present the comparison of the performance of physicians and AI in detection of those sounds.

A database of more than 10 000 recordings described by a consilium of specialists (pulmonologists and acousticians) was used for AI learning. Then another set of more than 500 real auscultatory sounds were used to investigate the efficiency of AI in comparison to a group of doctors. The standard F1-score was used for evaluation, because it considers both the precision and the recall. For each phenomena, the results for the AI is higher than for doctors with an average advantage of 8.4 percentage points, reaching even 13,5 p.p. for fine crackles.

The results suggest that the implementation of AI can significantly improve the efficiency of auscultation in everyday practice making it more objective, leading to a minimization of errors. The solution is now being tested with a group of hospitals and medical providers and proves its efficiency and usability in everyday practice making this examination faster and more reliable.

Tomasz Grzywalski, Marcin Szajek, Honorata Hafke-Dys, Anna Bręborowicz, Jędrzej Kociński, Anna Pastusiak, Riccardo Belluzzo

Artificial Intelligence in Medicine

Fully Interactive Lungs Auscultation with AI Enabled Digital Stethoscope

Performing an auscultation of respiratory system normally requires the presence of an experienced doctor, but the most recent advances in artificial intelligence (AI) open up a possibility for the laymen to perform this procedure by himself in home environment. However, to make it feasible, the system needs to include two main components: an algorithm for fast and accurate detection of breath phenomena in stethoscope recordings and an AI agent that interactively guides the end user through the auscultation process. In this work we present a system that solves both of these problems using state-of-the-art machine learning al gorithms. Our breath phenomena detection model was trained on 5000 stethoscope recordings of both sick (hospitalized) and healthy children. All recordings were labeled by a pulmonologist and acousticians. Trained model shows nearly optimal performance in terms of both sensitivity and specificity when tested on unseen recordings. The agent is able to accurately assess patient’s lung health status by auscultating only 3 out of 12 locations on average. The decision about each next auscultation location or end of examination is made dynamically, after each recording, based on breath phenomena detected so far. This allows the agent to make best prediction even if the auscultation is time-constrained.

Tomasz Grzywalski, Riccardo Belluzzo, Mateusz Piecuch, Marcin Szajek, Anna Bręborowicz, Anna Pastusiak, Honorata Hafke-Dys, Jędrzej Kociński

Conference on Agents and Artificial Intelligence - ICAART

Interactive Lungs Auscultation with Reinforcement Learning Agent

To perform a precise auscultation for the purposes of examination of respiratory system normally requires the presence of an experienced doctor. With most recent advances in machine learning and artificial intelligence, automatic detection of pathological breath phenomena in sounds recorded with stethoscope becomes a reality. But to perform a full auscultation in home environment by layman is another matter, especially if the patient is a child. In this paper we propose a unique application of Reinforcement Learning for training an agent that interactively guides the end user throughout the auscultation procedure. We show that intelligent selection of auscultation points by the agent reduces time of the examination fourfold without significant decrease in diagnosis accuracy compared to exhaustive auscultation.

Tomasz Grzywalski, Riccardo Belluzzo, Szymon Drgas, Agnieszka Cwalińska, Honorata Hafke-Dys

IEEE International Conference on Big Data

Parameterization of Sequence of MFCCs for DNN-based voice disorder detection

In this article a DNN-based system for detection of three common voice disorders (vocal nodules, polyps and cysts; laryngeal neoplasm; unilateral vocal paralysis) is presented. The input to the algorithm is (at least 3-second long) audio recording of sustained vowel sound /a:/. The algorithm was developed as part of the ”2018 FEMH Voice Data Challenge” organized by Far Eastern Memorial Hospital and obtained score value (defined in the challenge specification) of 77.44. This was the second best result before final submission. Final challenge results are not yet known during writing of this document. The document also reports changes that were made for the final submission which improved the score value in cross-validation by 0.6% points.

Tomasz Grzywalski., Adam Maciaszek, Adam Biniakowski, JanOrwat, Szymon Drgas, Mateusz Piecuch, Riccardo Belluzzo, Krzysztof Joachimiak, Dawid Niemiec, Jakub Ptaszyński, Krzysztof Szarzyński

Biochemistry, Molecular Biology & Allergy

Opportunities for domestic monitoring of children with an electronic stethoscope with automatic auscultation sound analysis system

In case of children suffering from chronic diseases of respiratory system, including asthma, it is very important to track any changes in the respiratory system condition. Domestic patient monitoring is becoming more and more popular. It is much more comfortable for patients who are less stressed, being relieved from any necessity to attend doctor’s offices, and are not exposed to pathogens present in medical facilities. Furthermore, it is also important for the attending physician who is provided with documented data. Until now, any aggravation of a past disease has been reported by children’s parents during medical appointments. Such method for providing information entails potential miscommunication, misjudgement and highly biased evaluation. a solution might be an electronic stethoscope, providing easy way to examine children in domestic conditions and to record auscultation results. Currently, it is possible to record auscultation sounds, provide a doctor with remote access to such records, and also to report any appearance of specific sounds and their intensity. Based on collaboration with scientific centres, there is a solution being developed: StethoMe^®, a smart stethoscope, designed to provide a patient with a method for domestic auscultation. This system enables recording of auscultation sounds, submitting them to a physician and automatic classification of recorded sounds in four lasses: wheezes, fine crackles, coarse crackles and rhonchi, according to [1]. a physician may see a panel with provided access to sounds, their spectrograms, being visualisations of sounds facilitating their interpretation, and also an algorithm report, related to potential appearance of specific pathologies. This solutions is currently under development and in a testing phase in Europe.