Richard is based in Melbourne, Australia and has a strong interest in film, VR and new media. He has written for an number of online and print publications over the last decade and also acted as film critic for several radio broadcasters and podcasts. Richard was Chair of the Australian Film Critics Association for two years (2013-2015) and when not writing or making videos for New Atlas he can be found in darkened cinemas yelling at the screen.
A new algorithm significantly outperformed human clinicians in predicting which patients would go on to develop Alzheimer’s disease
A powerful new deep learning algorithm has been developed that can study PET scan images and effectively detect the onset of Alzheimer’s disease up to six years earlier than current diagnostic methods. The research is part of a new wave of work using machine learning technology to identify subtle patterns in complex medical imaging data that human clinicians are unable to pick up.
One of the clearer diagnostic tools we currently have to identify the onset of Alzheimer’s disease is a type of brain imaging scan called an 18-F-fluorodeoxyglucose PET scan (FDG-PET). This scan is traditionally used to identify several types of cancers but in recent years has proved itself useful in identifying Alzheimer’s, as well as several other types of dementia.
This new research trained a machine learning algorithm on over 2100 FDG-PET brain images. While human clinicians are adept at evaluating these scans, co-author on the new study Jae Ho Sohn says new deep learning technology has the ability to identify more subtle patterns in dense imaging data.
“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” says Jae Ho Sohn. “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”
When the algorithm was ultimately tested on a small independent set of brain scans it was able to predict every single case that advanced to Alzheimer’s on average around six years earlier than the disease was eventually diagnosed. On this metric, the algorithm significantly outperformed human radiologists.
While other researchers have applauded these promising results, many are urging caution, suggesting much more work needs to be done to validate the results before it moves into clinical applications.
“This is a tiny data set, only looking at 40 people,” says John Hardy from University College London. “It’s also a very selected data set and not representative of the whole population. So we can’t know yet whether this is relevant to most people.”
It is also important to note that these kinds of PET scans are not broadly available to most patients. This kind of machine learning innovation is undeniably impressive in an academic context but it doesn’t offer up a useful tool for doctors hoping to better diagnose patients en masse.
“Currently in the UK, the use of PET scanning is mainly limited to research studies and clinical trials, to ensure that potential new medicines are tested in the right people,” explains Carol Routledge, from Alzheimer’s Research UK. “PET scans are a powerful tool, but they are expensive and require specialist facilities and expertise.”
However, this study does offer yet another indication that deep learning algorithms can greatly assist clinicians in wading through the growing mass of big data that is rapidly being accumulated. Alongside a recent study from McGill University revealing an algorithm that can evaluate an assortment of diagnostic data to predict the earliest stages of cognitive decline, this new research adds to a body of work that is recruiting computers to help us predict neurodegenerative disease before major symptoms take hold.
The new study was published in the journal Radiology.
By Apple All of Apple's original TV shows, series, and moviesTowards the end of March 2019, Apple announced its new streaming service, called Apple TV...
Deep learning algorithm detects Alzheimer’s up to six years before doctors
sherry 0 Data Science, Engineering, Technology,
By Rich Haridy
Richard is based in Melbourne, Australia and has a strong interest in film, VR and new media. He has written for an number of online and print publications over the last decade and also acted as film critic for several radio broadcasters and podcasts. Richard was Chair of the Australian Film Critics Association for two years (2013-2015) and when not writing or making videos for New Atlas he can be found in darkened cinemas yelling at the screen.
A powerful new deep learning algorithm has been developed that can study PET scan images and effectively detect the onset of Alzheimer’s disease up to six years earlier than current diagnostic methods. The research is part of a new wave of work using machine learning technology to identify subtle patterns in complex medical imaging data that human clinicians are unable to pick up.
One of the clearer diagnostic tools we currently have to identify the onset of Alzheimer’s disease is a type of brain imaging scan called an 18-F-fluorodeoxyglucose PET scan (FDG-PET). This scan is traditionally used to identify several types of cancers but in recent years has proved itself useful in identifying Alzheimer’s, as well as several other types of dementia.
This new research trained a machine learning algorithm on over 2100 FDG-PET brain images. While human clinicians are adept at evaluating these scans, co-author on the new study Jae Ho Sohn says new deep learning technology has the ability to identify more subtle patterns in dense imaging data.
“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” says Jae Ho Sohn. “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”
When the algorithm was ultimately tested on a small independent set of brain scans it was able to predict every single case that advanced to Alzheimer’s on average around six years earlier than the disease was eventually diagnosed. On this metric, the algorithm significantly outperformed human radiologists.
While other researchers have applauded these promising results, many are urging caution, suggesting much more work needs to be done to validate the results before it moves into clinical applications.
“This is a tiny data set, only looking at 40 people,” says John Hardy from University College London. “It’s also a very selected data set and not representative of the whole population. So we can’t know yet whether this is relevant to most people.”
It is also important to note that these kinds of PET scans are not broadly available to most patients. This kind of machine learning innovation is undeniably impressive in an academic context but it doesn’t offer up a useful tool for doctors hoping to better diagnose patients en masse.
“Currently in the UK, the use of PET scanning is mainly limited to research studies and clinical trials, to ensure that potential new medicines are tested in the right people,” explains Carol Routledge, from Alzheimer’s Research UK. “PET scans are a powerful tool, but they are expensive and require specialist facilities and expertise.”
However, this study does offer yet another indication that deep learning algorithms can greatly assist clinicians in wading through the growing mass of big data that is rapidly being accumulated. Alongside a recent study from McGill University revealing an algorithm that can evaluate an assortment of diagnostic data to predict the earliest stages of cognitive decline, this new research adds to a body of work that is recruiting computers to help us predict neurodegenerative disease before major symptoms take hold.
The new study was published in the journal Radiology.
Source: Radiological Society of North America
Links:
Tags: 18-F-fluorodeoxyglucose PET scan (FDG-PET), academia, administrative perspectives, AI, AI methods, algorithm, alphr.com, Alzheimer’s disease, Alzheimer’s Research UK, analytics, analytics space, applied machine learning, artificial intelligence, Australia, Automation of Machine-Learning, Autonomous driving, barchester.com, Big Data Analytics, broad spectrum, Carol Routledge, clinical applications, comments, complex medical imaging data, Conference, content, Continuous delivery, Cyber, Data Science, Data Scientists, deep learning, DEEP LEARNING ALGORITHM DETECTS ALZHEIMER’S UP TO SIX YEARS BEFORE DOCTORS, deep learning algorithms, delivering, DESIGN, detect, develop, Developers, DevOps, diagnosed, Dialogue Bots, domains, Education, effectively, engineering, Ethics of artificial intelligence, excellent, experience, Explaining of Israel, FDG-PET brain images, Fintech, focus, foxnews.com, Future of AI, great success, Healthcare, helpful feedback, high-quality, ideas, identify more subtle patterns in dense imaging data, identify several types of cancers, identify subtle patterns, improvement, industry, industry tracks, innovation, Intelligent robots, iot, Jae Ho Sohn, Journal Radiology, Large scale analytics, leading experts, links, Machine ethics, machine learning innovation, machine learning technology, major symptoms, McGill University, Melbourne, Natural Language Understanding, neurodegenerative disease, new algorithm, New Atlas, new media, nypost.com, online, organizing conference, outperformed human clinicians, participants, patients, PET scanning, PET scans, population, powerful new deep learning algorithm, predict, predicting, predictive applications, print publications, Radiological Society of North America, radiologybusiness.com, real world, real-world domains, Registration, reinforcement learning, research and application, research innovations, research track, researchers, Retail, Rich Haridy, Richard, Robot rights, sciencedaily.com, screen, selected data set, several other types of dementia, significantly, Source, sponsorship, state-of-the-art, strong interest in film, subtle process, Systems for ML, technical presentations, TECHNOLOGY, telegraph.co.uk, the algorithm significantly outperformed human radiologists, The conference, The new study, The Summit, thesun.co.uk, Threat to human dignity, to predict, topics, tutorial, vr, was published, Weaponization of AI
Related posts
WinAI 2019
Apple TV+ Guide: All the shows and movies available now!
About The Author
sherry
CODESIGN.BLOG