Artificial intelligence can now predict people’s lifespan by browsing images of their organs
HI-TECH computers can now predict your lifespan by examining images of your organs — but the Adelaide researchers aren’t quite sure how they are doing it.
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COMPUTERS armed with artificial intelligence are closing in on being able to predict how long you will live, by examining images of your organs.
University of Adelaide researchers, along with Australian and international collaborators, used the ‘thinking robots’ 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 per cent accuracy — comparable to ‘manual’ predictions by clinicians.
The computers are so smart that researchers admit they do not know exactly what the machines were looking at to make the predictions, noting they are processing massive amounts of data to detect subtle changes.
Researchers from University’s School of Public Health and School of Computer Science say a computer’s ability to predict a patient’s lifespan simply by looking at images of their organs now is a step closer to becoming a reality.
The work, published in the Nature journal Scientific Reports, has implications for the early diagnosis of serious illness and subsequent treatment.
It is the first study of its kind using medical images and artificial intelligence, according to lead author Dr Luke Oakden-Rayner, a radiologist and PhD student with the University of Adelaide’s School of Public Health.
“Predicting the future of a patient is useful because it may enable doctors to tailor treatments to the individual,” he said.
“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.”
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 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 researchers hope to apply the same techniques to predict other major medical conditions, such as the onset of heart attacks.
The next stage of their research involves analysing tens of thousands of patient images.