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Artificial Intelligence Predicts Patient Lifespans

Summary: Researchers report artificial intelligence systems are able to predict a person’s life span by reviewing CT images of their organs.

Source: University of Adelaide.

A computer’s ability to predict a patient’s lifespan simply by looking at images of their organs is a step closer to becoming a reality, thanks to new research led by the University of Adelaide.

The research, now published in the Nature journal Scientific Reports, has implications for the early diagnosis of serious illness, and medical intervention.

Researchers from the University’s School of Public Health and School of Computer Science, along with Australian and international collaborators, used artificial intelligence 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% 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,” 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 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,” Dr Oakden-Rayner says.

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 says.

Image shows a woman and computer code.

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. NeuroscienceNews.com image is for illustrative purposes only.

“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 important medical conditions, such as the onset of heart attacks.

The next stage of their research involves analysing tens of thousands of patient images.

About this neuroscience research article

Source: David Ellis – University of Adelaide
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Full open access research for “Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework” by Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C. Nascimento, Andrew P. Bradley & Lyle J. Palmer in Scientific Reports. Published online May 10 2017 doi:10.1038/s41598-017-01931-w

Cite This NeuroscienceNews.com Article
University of Adelaide “Artificial Intelligence Predicts Patient Lifespans.” NeuroscienceNews. NeuroscienceNews, 5 June 2017.
<http://neurosciencenews.com/artificial-intelligence-life-span-6833/>.
University of Adelaide (2017, June 2). Artificial Intelligence Predicts Patient Lifespans. NeuroscienceNew. Retrieved June 5, 2017 from http://neurosciencenews.com/artificial-intelligence-life-span-6833/
University of Adelaide “Artificial Intelligence Predicts Patient Lifespans.” http://neurosciencenews.com/artificial-intelligence-life-span-6833/ (accessed June 5, 2017).

Abstract

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous ‘manual’ clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research – mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

“Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework” by Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C. Nascimento, Andrew P. Bradley & Lyle J. Palmer in Scientific Reports. Published online May 10 2017 doi:10.1038/s41598-017-01931-w

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