User login
Can asthma symptoms be monitored reliably at home? Until now, the answer would have been yes, but not in preschool-age patients. Recent findings in Annals of Family Medicine suggest that this limitation can be overcome with the assistance of artificial intelligence (AI).
Objectivity Challenge
A timely diagnosis of asthma exacerbations, which is crucial for proper disease management, requires effective home monitoring. While some lung function parameters, like peak expiratory flow (PEF), can be measured by patients at home, tools for this purpose are not designed for very young children.
“To achieve effective asthma management, patients should be given the necessary tools to allow them to recognize and respond to worsening asthma,” wrote the study authors. Despite the Global Initiative for Asthma identifying respiratory sounds as a fundamental parameter for exacerbation recognition, these are almost exclusively evaluated during doctor visits. Recognizing respiratory sounds and judging whether there has been a change can be challenging for those outside the medical profession.
To enhance home monitoring, researchers from the Department of Pediatric Pneumology and Rheumatology at the University of Lublin, Poland, experimented with the StethoMe stethoscope, which enables the recognition of pathologic signs, including continuous and transient noises. This AI-assisted stethoscope, trained on over 10,000 respiratory sound recordings, is certified as a Class IIa medical device in Europe.
The ‘Smart’ Stethoscope
The 6-month study enlisted 149 patients with asthma (90 children and 59 adults). Participants self-monitored (but parents or caregivers managed for children) once daily in the first 2 weeks and at least once weekly thereafter using three tools. The first was the StethoMe stethoscope, which was used for detecting respiratory sounds, respiratory rate (RR), heart rate (HR), and inspiration/expiration ratio (I/E). Patients were provided a “map” of chest points at which to position the stethoscope. The second was a pulse oximeter, which was used to measure oxygen saturation. The third was a peak flow meter for quantifying PEF. Simultaneously, a health questionnaire was completed.
Data from 6029 completed self-monitoring sessions were used to determine the most effective parameter for exacerbation recognition, quantified by the area under the receiver operating characteristic curve (AUC). The researchers concluded that the parameter with the best performance was wheeze intensity in young children (AUC 84%, 95% CI, 82%-85%), wheeze intensity in older children (AUC, 81%; 95% CI, 79%-84%), and questionnaire response for adults (AUC, 92%; 95% CI, 89%-95%). Combining multiple parameters increased effectiveness.
“The present results clearly show that a set of parameters (wheezes, rhonchi, coarse and fine crackles, HR, RR, and I/E) measured by a device such as an AI-aided home stethoscope allows for the detection of exacerbations without the need for performing PEF measurements, which can be equivocal,” the study authors concluded. “In addition, in the case of younger children (age, < 5 years), when introduced on a large scale, the analyzed home stethoscope appears to be a promising tool that might make asthma diagnosis more straightforward and substantially facilitate asthma monitoring.”
A version of this article first appeared on Medscape.com. This article was translated from Univadis Italy, which is part of the Medscape professional network.
Can asthma symptoms be monitored reliably at home? Until now, the answer would have been yes, but not in preschool-age patients. Recent findings in Annals of Family Medicine suggest that this limitation can be overcome with the assistance of artificial intelligence (AI).
Objectivity Challenge
A timely diagnosis of asthma exacerbations, which is crucial for proper disease management, requires effective home monitoring. While some lung function parameters, like peak expiratory flow (PEF), can be measured by patients at home, tools for this purpose are not designed for very young children.
“To achieve effective asthma management, patients should be given the necessary tools to allow them to recognize and respond to worsening asthma,” wrote the study authors. Despite the Global Initiative for Asthma identifying respiratory sounds as a fundamental parameter for exacerbation recognition, these are almost exclusively evaluated during doctor visits. Recognizing respiratory sounds and judging whether there has been a change can be challenging for those outside the medical profession.
To enhance home monitoring, researchers from the Department of Pediatric Pneumology and Rheumatology at the University of Lublin, Poland, experimented with the StethoMe stethoscope, which enables the recognition of pathologic signs, including continuous and transient noises. This AI-assisted stethoscope, trained on over 10,000 respiratory sound recordings, is certified as a Class IIa medical device in Europe.
The ‘Smart’ Stethoscope
The 6-month study enlisted 149 patients with asthma (90 children and 59 adults). Participants self-monitored (but parents or caregivers managed for children) once daily in the first 2 weeks and at least once weekly thereafter using three tools. The first was the StethoMe stethoscope, which was used for detecting respiratory sounds, respiratory rate (RR), heart rate (HR), and inspiration/expiration ratio (I/E). Patients were provided a “map” of chest points at which to position the stethoscope. The second was a pulse oximeter, which was used to measure oxygen saturation. The third was a peak flow meter for quantifying PEF. Simultaneously, a health questionnaire was completed.
Data from 6029 completed self-monitoring sessions were used to determine the most effective parameter for exacerbation recognition, quantified by the area under the receiver operating characteristic curve (AUC). The researchers concluded that the parameter with the best performance was wheeze intensity in young children (AUC 84%, 95% CI, 82%-85%), wheeze intensity in older children (AUC, 81%; 95% CI, 79%-84%), and questionnaire response for adults (AUC, 92%; 95% CI, 89%-95%). Combining multiple parameters increased effectiveness.
“The present results clearly show that a set of parameters (wheezes, rhonchi, coarse and fine crackles, HR, RR, and I/E) measured by a device such as an AI-aided home stethoscope allows for the detection of exacerbations without the need for performing PEF measurements, which can be equivocal,” the study authors concluded. “In addition, in the case of younger children (age, < 5 years), when introduced on a large scale, the analyzed home stethoscope appears to be a promising tool that might make asthma diagnosis more straightforward and substantially facilitate asthma monitoring.”
A version of this article first appeared on Medscape.com. This article was translated from Univadis Italy, which is part of the Medscape professional network.
Can asthma symptoms be monitored reliably at home? Until now, the answer would have been yes, but not in preschool-age patients. Recent findings in Annals of Family Medicine suggest that this limitation can be overcome with the assistance of artificial intelligence (AI).
Objectivity Challenge
A timely diagnosis of asthma exacerbations, which is crucial for proper disease management, requires effective home monitoring. While some lung function parameters, like peak expiratory flow (PEF), can be measured by patients at home, tools for this purpose are not designed for very young children.
“To achieve effective asthma management, patients should be given the necessary tools to allow them to recognize and respond to worsening asthma,” wrote the study authors. Despite the Global Initiative for Asthma identifying respiratory sounds as a fundamental parameter for exacerbation recognition, these are almost exclusively evaluated during doctor visits. Recognizing respiratory sounds and judging whether there has been a change can be challenging for those outside the medical profession.
To enhance home monitoring, researchers from the Department of Pediatric Pneumology and Rheumatology at the University of Lublin, Poland, experimented with the StethoMe stethoscope, which enables the recognition of pathologic signs, including continuous and transient noises. This AI-assisted stethoscope, trained on over 10,000 respiratory sound recordings, is certified as a Class IIa medical device in Europe.
The ‘Smart’ Stethoscope
The 6-month study enlisted 149 patients with asthma (90 children and 59 adults). Participants self-monitored (but parents or caregivers managed for children) once daily in the first 2 weeks and at least once weekly thereafter using three tools. The first was the StethoMe stethoscope, which was used for detecting respiratory sounds, respiratory rate (RR), heart rate (HR), and inspiration/expiration ratio (I/E). Patients were provided a “map” of chest points at which to position the stethoscope. The second was a pulse oximeter, which was used to measure oxygen saturation. The third was a peak flow meter for quantifying PEF. Simultaneously, a health questionnaire was completed.
Data from 6029 completed self-monitoring sessions were used to determine the most effective parameter for exacerbation recognition, quantified by the area under the receiver operating characteristic curve (AUC). The researchers concluded that the parameter with the best performance was wheeze intensity in young children (AUC 84%, 95% CI, 82%-85%), wheeze intensity in older children (AUC, 81%; 95% CI, 79%-84%), and questionnaire response for adults (AUC, 92%; 95% CI, 89%-95%). Combining multiple parameters increased effectiveness.
“The present results clearly show that a set of parameters (wheezes, rhonchi, coarse and fine crackles, HR, RR, and I/E) measured by a device such as an AI-aided home stethoscope allows for the detection of exacerbations without the need for performing PEF measurements, which can be equivocal,” the study authors concluded. “In addition, in the case of younger children (age, < 5 years), when introduced on a large scale, the analyzed home stethoscope appears to be a promising tool that might make asthma diagnosis more straightforward and substantially facilitate asthma monitoring.”
A version of this article first appeared on Medscape.com. This article was translated from Univadis Italy, which is part of the Medscape professional network.
FROM ANNALS OF FAMILY MEDICINE