MELBOURNE, Australia, Feb.
21, 2017 /PRNewswire/ -- IBM (NYSE: IBM) Research has
today announced new research developments in IBM Watson's ability
to detect abnormalities of the eye's retina. The Melbourne based IBM researchers have trained a
research version of Watson to recognize abnormalities in retina
images, which could in the future offer doctors greater insights
and speed in their early identification of patients who may be at
risk of eye diseases – such as glaucoma, a leading cause of
blindness in the developed world.
The research began in 2015 and the latest work has focused on
streamlining some of the manual processes experienced by doctors
today. This includes distinguishing between left and right eye
images1, evaluating the quality of retina
scans2, as well as ranking possible indicators of
glaucoma3. Glaucoma has been named "the silent thief of
sight" as many patients remain undiagnosed until irreversible
vision loss occurs. Glaucoma can be treated but early detection is
critical, with doctors currently relying on regular eye examination
screening programs.
The researchers applied deep learning techniques and image
analytics technology to 88,000 de-identified retina images accessed
through EyePACS®, to analyze key anomalies of the eye.
The research results demonstrate Watson's ability to accurately
measure the ratio of the optic cup to disc – which is a key sign of
glaucoma – with statistical performance as high as 95 percent. The
technology has also been trained to distinguish between left and
right eye images (with up to 94 percent confidence), which are
important for downstream analysis and for the development effective
treatment programs.
"It is estimated that at least 150,000 Australians have
undiagnosed glaucoma, with numbers expected to rise due to our
rapidly aging population. It is critical that every Australian has
access to regular eye examinations throughout their life so that
diseases like glaucoma and diabetic retinopathy can be detected and
treated as early as possible," said Dr. Peter van Wijngaarden, Principal Investigator at
Centre for Eye Research Australia, Department of Ophthalmology,
University of Melbourne.
"There is a real need for resources that allow all Australians
to access regular eye examinations and the development of image
analytics and deep learning technology will provide great promise
in this area."
The research is expected to continue to improve over time as the
research technology expands to detect features of other eye
diseases such as diabetic retinopathy and age-related macular
degeneration.
"Medical image analysis with cognitive technology has the
capacity to fundamentally change the delivery of healthcare
services," said Dr. Joanna Batstone,
Vice President and Lab Director at IBM Research Australia. "Medical
images represent a rich source of data for clinicians to make early
diagnosis and treatment of disease, from assessing the risk of
melanomas to identifying eye diseases through the analysis of
retinas. Cognitive technology holds immense promise for confirming
the accuracy, reproducibility and efficiency of clinicians'
analyses during the diagnostic workflow."
IBM Research globally continues to advance research combining
cognitive technology with medical images. Through its 12
collaborative labs worldwide, IBM Research is focused on research
projects involving medical imaging analysis for diseases such as
melanoma, breast cancer, lung cancer and eye disease.
About IBM Research
For more than seven decades, IBM
Research has defined the future of information technology with more
than 3,000 researchers in 12 labs located across six continents.
Scientists from IBM Research have produced six Nobel Laureates, 10
U.S. National Medals of Technology, five U.S. National Medals of
Science, six Turing Awards, 19 inductees in the U.S. National
Academy of Sciences and 20 inductees into the U.S. National
Inventors Hall of Fame. For more information about IBM Research,
visit www.ibm.com/research.
The research results were presented at the 38th IEEE Annual
International Conference of the Engineering in Medicine and
Biology Society (EMBC), 19th International Conference on Medical
Image Computing and Computer Assisted Intervention (MICCAI) and the
Digital Image Computing: Techniques and Applications (DICTA) in
Australia.
____________________________
|
1
P. Roy, et al. "Automatic Eye Type Detection in Retinal
Fundus Image Using Fusion of Transfer Learning and Anatomical
Features." International Conference on Digital Image Computing:
Techniques and Applications (DICTA), 2016.
http://ieeexplore.ieee.org/abstract/document/7797012/
|
2 D.
Mahapatra et al. "Retinal Image Quality
Classification Using Saliency Maps and
CNNs." Machine Learning in Medical
Imaging, Volume 10019 of the series Lecture Notes in
Computer Science, pp 172-179.
http://link.springer.com/chapter/10.1007/978-3-319-47157-0_21.
|
3
S. Sedai, et al. "Segmentation of Optic Disc and Optic
Cup in Retinal Fundus Images Using Coupled Shape
Regression." Proceedings of the Ophthalmic Medical
Image Analysis Third International Workshop (OMIA 2016) Held in
Conjunction with MICCAI 2016, pp 1-8.
http://ir.uiowa.edu/omia/2016_Proceedings/2016/1/
|
CONTACT: Adrienne C. Sabilia,
acsabili@us.ibm.com, 1-914-499-6399
To view the original version on PR Newswire,
visit:http://www.prnewswire.com/news-releases/ibm-research-is-training-watson-to-identify-eye-retina-abnormalities-300410967.html
SOURCE IBM