The smartphone app that can distinguish between respiratory diseases in children by analysing soundwaves in their coughs

Cough analyser app 'is better than doctors at diagnosing children with conditions such as asthma, pneumonia and bronchitis' and is 97

The smartphone app that can distinguish between respiratory diseases in children by analysing soundwaves in their coughs
Cough analyser app 'is better than doctors at diagnosing children with conditions such as asthma, pneumonia and bronchitis' and is 97

How does it work?
The Software analyses sounds of child's cough and provides on the spot diagnosis
The app works by listening to youngsters coughing into the device's microphone and analysing the sounds associated with five conditions.

A trial found it could distinguish between asthma, bronchiolitis, pneumonia, croup and lower respiratory tract disease with up to 97 per cent accuracy.


How did the App do against doctors?
It outperformed panel of doctors in accurately diagnosing five different diseases
Researchers found it outperformed senior doctors at diagnosing all five of the conditions.

It can be difficult to differentiate between respiratory disorders in children, even for experienced doctors.

The future
Hoped app will allow treatments to begin sooner by removing physical exam

Scientists hope, by removing the need for a physical examination, the app will allow treatments to begin sooner.
Researchers at Curtin University and The University of Queensland, Australia, programmed the app to recognise the soundwaves in coughs, similar to speech recognition technology.

They then used the app to categorise the coughs of 585 children between ages 29 days to 12 years, who were being cared for at two hospitals in Western Australia.

Recordings were done in realistic hospital environments with background noises including talking, crying and medical devices.

The accuracy of the app was determined by comparing its results with the diagnosis of a panel of doctors.

The doctors were allowed to examine the patients and review results of imaging, laboratory findings and hospital charts.

It had 97 per cent accuracy in spotting asthma, compared to 91 per cent by doctors and 87 per cent for pneumonia, compared to 85 per cent by the panel.

It scored 83 per cent accuracy when picking up on lower respiratory tract disease, compared with 82 per cent by doctors.

And it was 85 per cent accurate in spotting croup, compared to 82 per cent by the panel.

The tech also snuffed out bronchiolitis 84 per cent of the time, compared to 81 per cent by the team of experts.

Dr Paul Porter, lead author of the study, said: '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.

'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.'

Dr Samantha Walker, Director of Research and Policy at Asthma UK, said described the findings as 'exciting'.

She said: 'Asthma is very difficult to diagnose in children, particularly those under five, as the tools available for doctors to use are crude so this smartphone app is potentially an exciting development.

'Around one in five children have to wait three or more years for an asthma diagnosisis, which places a huge emotional and practical toll on parents as many see their children in and out of hospital, fighting for life.

'While this new tool shows how technology in healthcare has the potential to transform the lives of children with asthma, more research is needed to make sure that these findings are as reliable as possible and can be used in routine care practice.'

The findings were published in the journal Respiratory Research.