Scientists have developed a new artificial intelligence system that can predict how long a person will live simply by looking at the images of their organs.
The system, developed by researchers from University of Adelaide in Australia, analysed the medical imaging of 48 patients' chests and was able to predict which of them would die within five years, with 69 per cent accuracy.
This is comparable to 'manual' predictions by clinicians, researchers said.
The study, published in the journal Scientific Reports, has implications for the early diagnosis of serious illness and medical intervention.
"Predicting the future of a patient is useful because it may enable doctors to tailor treatments to the individual," said Luke Oakden-Rayner, PhD student at the University of Adelaide.
"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," said Oakden-Rayner.
"Our research has investigated the use of 'deep learning', a technique where computer systems can learn how to understand and analyse images," he said.
"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," he said.
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," 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," he said.
The researchers hope to apply the same techniques to predict other important medical conditions, such as the onset of heart attacks.
