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Deep learning is the fastest-growing field of artificial intelligence. It is a form of machine learning which uses algorithms to make sense of infinite amounts of data such as images, sound, and text.
We’ve really just begun to scratch the surface of how this technology will impact healthcare, but two collaborations announced earlier this year are hoping to move the science forward.
Google’s DeepMind Health has teamed up with London’s Moorfields Eye Hospital, on a five-year project to determine if machine learning can speed up and improve diagnosis of eye diseases by developing machine learning approaches to review eye scans.
The two identified targets of the research are diabetic retinopathy and age-related macular degeneration or AMD . Among other objectives, the project aims to develop an early warning system to predict the onset of wet AMD, enabling earlier treatment, significantly improving the patient outcomes
The second collaboration is between NVIDIA, the Silicon Valley-based developer of graphics processing techniques, and Massachusetts General Hospita in Boston.
The hospital recently established the MGH Clinical Data Science Center, which, according to its launch press release “is paving the way for a new approach to diagnosing and treating disease using cognitive computational algorithms such as machine learning and artificial neural networks.”
Researchers will use the NVIDIA DGX-1, a server designed for AI applications, along with deep learning algorithms created by NVIDIA engineers to train a deep neural network using phenotypic, genetics, and imaging data. To put this in context, the hospital has access to some 10 billion images which will be used in the research.
It is hoped that the outcomes from this project will provide physicians with incredibly powerful tools to assist with early diagnosis and accurate treatments for a variety of conditions.
Dr Keith Dreyer, the executive director of the MGH Centre is quoted in our source article as saying “We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation. Guided by precision health care, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations.”
Learn more about Deep Learning
Read the Source Article on Healthedgy.com
Healthedgy.com: Deep Learning, Big Data Projects Hone in Diseases
Cover Image Source:Searching for the Mind. Jon Lief M.D
We’ve really just begun to scratch the surface of how this technology will impact healthcare, but two collaborations announced earlier this year are hoping to move the science forward.
Google’s DeepMind Health has teamed up with London’s Moorfields Eye Hospital, on a five-year project to determine if machine learning can speed up and improve diagnosis of eye diseases by developing machine learning approaches to review eye scans.
The two identified targets of the research are diabetic retinopathy and age-related macular degeneration or AMD . Among other objectives, the project aims to develop an early warning system to predict the onset of wet AMD, enabling earlier treatment, significantly improving the patient outcomes
The second collaboration is between NVIDIA, the Silicon Valley-based developer of graphics processing techniques, and Massachusetts General Hospita in Boston.
The hospital recently established the MGH Clinical Data Science Center, which, according to its launch press release “is paving the way for a new approach to diagnosing and treating disease using cognitive computational algorithms such as machine learning and artificial neural networks.”
Researchers will use the NVIDIA DGX-1, a server designed for AI applications, along with deep learning algorithms created by NVIDIA engineers to train a deep neural network using phenotypic, genetics, and imaging data. To put this in context, the hospital has access to some 10 billion images which will be used in the research.
It is hoped that the outcomes from this project will provide physicians with incredibly powerful tools to assist with early diagnosis and accurate treatments for a variety of conditions.
Dr Keith Dreyer, the executive director of the MGH Centre is quoted in our source article as saying “We now have the ability to expand the field of radiology beyond its predominant state of providing visualization for human interpretation. Guided by precision health care, we are entering the radiological era of biometric quantification, where our interpretations will be enhanced by algorithms learned from the diagnostic data of vast patient populations.”
Learn more about Deep Learning
Read the Source Article on Healthedgy.com
Healthedgy.com: Deep Learning, Big Data Projects Hone in Diseases
Cover Image Source:Searching for the Mind. Jon Lief M.D
Compiled by Linda Ravenhill, 2 September 2016
