RESEARCHERS are a step closer to developing artificial intelligence that can predict a patient's lifespan - but would you really want to know?
Researchers from the University of Adelaide's School of Public Health and School of Computer Science, with Australian and international collaborators, used artificial intelligence to analyse the medical imaging of 48 patients' chests.
The computer-based analysis was able to predict which patients would die within five years, with 69 per cent 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," said 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."
While only a small sample was used for this study, Dr Oakden-Rayner said the research suggested the computer had learnt to recognise the complex imaging appearances of diseases - "something that requires extensive training for human experts".
The researchers could not identify exactly what the computer system was seeing in the images to make its predictions, however 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 said.
"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 next stage of the research will involve analysing tens of thousands of patient images.
Read the abstract HERE
