Date:
June 1, 2017
Source:
University of Adelaide
Summary:
A computer's ability to predict a patient's lifespan simply
by looking at images of their organs is a step closer to becoming a reality,
thanks to new research.
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This is the first study of its kind
using medical images and artificial intelligence.
Credit: © iQoncept / Fotolia
A computer's ability to predict a
patient's lifespan simply by looking at images of their organs is a step closer
to becoming a reality, thanks to new research led by the University of
Adelaide.
The research, now published in the Nature
journal Scientific Reports, has implications for the early diagnosis of
serious illness, and medical intervention.
Researchers from the University's
School of Public Health and School of Computer Science, along with Australian
and international collaborators, used artificial intelligence to analyse the
medical imaging of 48 patients' chests. This computer-based analysis was able
to predict which patients would die within five years, with 69% accuracy --
comparable to 'manual' predictions by clinicians.
This is the first study of its kind
using medical images and artificial intelligence.
"Predicting the future of a
patient is useful because it may enable doctors to tailor treatments to the
individual," says lead author Dr Luke Oakden-Rayner, a radiologist and PhD
student with the University of Adelaide's School of Public Health.
"The accurate assessment of
biological age and the prediction of a patient's longevity has so far been
limited by doctors' inability to look inside the body and measure the health of
each organ.
"Our research has investigated
the use of 'deep learning', a technique where computer systems can learn how to
understand and analyse images.
"Although for this study only a
small sample of patients was used, our research suggests that the computer has
learnt to recognise the complex imaging appearances of diseases, something that
requires extensive training for human experts," Dr Oakden-Rayner says.
While the researchers could not
identify exactly what the computer system was seeing in the images to make its
predictions, the most confident predictions were made for patients with severe
chronic diseases such as emphysema and congestive heart failure.
"Instead of focusing on
diagnosing diseases, the automated systems can predict medical outcomes in a
way that doctors are not trained to do, by incorporating large volumes of data
and detecting subtle patterns," Dr Oakden-Rayner says.
"Our research opens new avenues
for the application of artificial intelligence technology in medical image
analysis, and could offer new hope for the early detection of serious illness,
requiring specific medical interventions."
The researchers hope to apply the
same techniques to predict other important medical conditions, such as the
onset of heart attacks.
The next stage of their research
involves analysing tens of thousands of patient images.
Story Source:
Materials provided by University
of Adelaide. Note: Content may be edited for style and
length.
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