Artificial
intelligence predicts patient lifespans
University of Adelaide
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 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.
