Respiratory disorders in children can now be detected via smartphone
The diagnosis of childhood respiratory disorders can now be readily done with an automated cough analysis technology incorporated in a smartphone app, according to a recent study. This system delivers high diagnostic accuracy ( between 81-97%) in detecting respiratory disorders, including pneumonia, RAD, croup, bronchitis, upper and lower respiratory tract disorders.
In children, respiratory disorders are the 2nd most common reason for admission to the emergency department and is a major disease burden worldwide. The differential diagnosis of respiratory disorders can be challenging even for experienced clinicians with access to diagnostic support services.
Diagnostic delays and errors can result in an increased risk of morbidity, antibiotic stewardship and mortality.
Researchers compared diagnoses made by the algorithm to those from a clinical adjudication panel ( who had access to all medical records and diagnostic support service results) in order to determine positive and negative percent agreement for a number of respiratory conditions.
It can be difficult to differentiate between respiratory disorders in children, even for experienced doctors.This study demonstrates how new technology, mathematical concepts, machine learning and clinical medicine can be successfully combined to produce completely new diagnostic tests utilising the expertise of several disciplines, said a Dr.
To develop the app, the authors used similar technology to that used in speech recognition, which they trained to recognise features of coughs which are characteristic of 5 different respiratory diseases. The researchers then used the app to categorise the coughs of 585 children between ages 29 days to 12 years who were being cared for at 2 hospitals.
The accuracy of the automated cough analyser was determined by comparing its diagnosis to a diagnosis reached by a panel of pediatricians after they had reviewed results of imaging, laboratory findings, hospital charts and conducted all available clinical investigations.
The authors note that the technology developed for this study is able to provide a diagnosis without the need for a clinical examination by a doctor in person, addressing a major limiting feature of existing telehealth consultations, which are used to provide clinical services remotely. removing the need for a clinical examination may allow targeted treatments to begin sooner.
The Dr. said, as the tool does not rely on clinical investigations, it can be used by health care providers of all levels of training and expertise. However, we would advise that where possible the tool should be used in conjunction with a clinician to maximise the clinical accuracy.
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In children, respiratory disorders are the 2nd most common reason for admission to the emergency department and is a major disease burden worldwide. The differential diagnosis of respiratory disorders can be challenging even for experienced clinicians with access to diagnostic support services.
Diagnostic delays and errors can result in an increased risk of morbidity, antibiotic stewardship and mortality.
Researchers compared diagnoses made by the algorithm to those from a clinical adjudication panel ( who had access to all medical records and diagnostic support service results) in order to determine positive and negative percent agreement for a number of respiratory conditions.
It can be difficult to differentiate between respiratory disorders in children, even for experienced doctors.This study demonstrates how new technology, mathematical concepts, machine learning and clinical medicine can be successfully combined to produce completely new diagnostic tests utilising the expertise of several disciplines, said a Dr.
To develop the app, the authors used similar technology to that used in speech recognition, which they trained to recognise features of coughs which are characteristic of 5 different respiratory diseases. The researchers then used the app to categorise the coughs of 585 children between ages 29 days to 12 years who were being cared for at 2 hospitals.
The accuracy of the automated cough analyser was determined by comparing its diagnosis to a diagnosis reached by a panel of pediatricians after they had reviewed results of imaging, laboratory findings, hospital charts and conducted all available clinical investigations.
The authors note that the technology developed for this study is able to provide a diagnosis without the need for a clinical examination by a doctor in person, addressing a major limiting feature of existing telehealth consultations, which are used to provide clinical services remotely. removing the need for a clinical examination may allow targeted treatments to begin sooner.
The Dr. said, as the tool does not rely on clinical investigations, it can be used by health care providers of all levels of training and expertise. However, we would advise that where possible the tool should be used in conjunction with a clinician to maximise the clinical accuracy.
THIS IS ONLY FOR INFORMATION, ALWAYS CONSULT YOU PHYSICIAN BEFORE HAVING ANY PARTICULAR FOOD/ MEDICATION/EXERCISE/OTHER REMEDIES. PS- THOSE INTERESTED IN RECIPES ARE FREE TO VIEW MY BLOG- https://gseasyrecipes.blogspot.com/ FOR INFO ABOUT KNEE REPLACEMENT, YOU CAN VIEW MY BLOG- https:// kneereplacement-stickclub.blogspot.com/
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Read more at Speciality Medical Dialogues: Respiratory disorders in children can now be detected via smartphone https://speciality.medicaldialogues.in/respiratory-disorders-in-children-can-now-be-detected-via-smartphone/
Labels: 5 different respiratory diseases, characteristics, Children, detected, diagnostic delays, expertise, many disciplines, respiratory disorders, smartphone app, speech recognition, uses
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