Thinking about how and when you’ll die might be morbid, but it has creeped into everyone’s mind at some point. Online tools like The Death Clock provide a very unscientific, and entertaining, prediction of your demise, but researchers have figured out a way to estimate a person’s lifespan with 69 percent accuracy.
In a very small study of 48 participants, all of whom were at least 60 years old, scientists from the University of Adelaide in Australia analyzed photos of people’s organs using artificial intelligence. They were able to predict who would die within five years with 69 percent accuracy, which is roughly the same as an oncologist’s.
Using deep learning, which involves inputting data into a computer system to help it make decisions, the researchers used radiological images because they provide undetectable clues, according to study co-author and epidemiologist Dr. Lyle Palmer, Ph.D, in a story on ResearchGate.
“Images are digital, standardized, and of very high quality—in fact, most images contain information that that the human eye literally can’t see,” he tells the site. “There is an enormous amount of potentially valuable information being collected that is not being fully utilized for diagnostic or prognostic purposes.”
So they used this untapped resource to uncover the hidden clues found in chest scans that human experts are unable to find. The computer looked for visual cues of diseases like cardiomegaly, an enlarged heart typically tied to high cholesterol. The team couldn’t decipher exactly what the computer detected to make its assessment, and while the study is small, researchers believe the finding is significant.
"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," says study co-author Dr. Oakden-Rayner, in a statement.
This research is a starting point for additional studies to look at thousands of cases.
“We intend to incorporate the clinical data routinely acquired with each scan and begin to predict other medical outcomes, like the development of strokes and heart attacks,” Palmer tells ResearchGate.
They hope that aside from predicting life span, this could be used to detect diseases early on.
“We want to one day use this technology to predict the onset of chronic diseases such as diabetes, heart disease, and cancer before any symptoms are evident,” says Palmer. In chronic conditions like cancer, early detection and treatment is the biggest factor between life and death.
Source: Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C. Nascimento, Andrew P. Bradley & Lyle J. Palmer. Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Nature, May 2017.
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