Using artificial intelligence to predict when a person will die based on medical images of patients’ internal organs is closer to development thanks to research from the University of Adelaide.

Researchers publishing their work in the Nature journal Scientific Report, used artificial intelligence to analyze the medical images of 48 patients' chests. With a 69 percent rate of accuracy, the analysis was able to predict which patients would die within the next five years.

"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 analyze images," Dr. Oakden-Rayner says. "Although for this study only a small sample of patients was used, our research suggests that the computer has learnt to recognize the complex imaging appearances of diseases, something that requires extensive training for human experts."

Although the researchers could not exactly determine what it was that the computer system was seeing in the images to make its predictions, the more confident predictions corresponded with those 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 ongoing research will next involve analyzing tens of thousands of patient images.