Biomedicine

Training Computers to Combat Blindness

(Page 2 of 2)

  • Friday, May 14, 2010
  • By Arlene Weintraub

The retina is particularly well suited to content-based image retrieval, Tobin says. Unlike other types of medical images, such as brain scans and mammograms--which are highly variable and require multiple images to create a three-dimensional effect--the retina is virtually two-dimensional and similar from one photo to another. That makes it easier for the technology to detect lesions, leaky blood vessels, swelling, and other abnormalities on the retina that can be early signs of disease. Chaum and Tobin spent five years developing algorithms that can extract information from retinal images and screen it against a database of more than 20,000 photos. AMDx doesn't produce diagnoses but rather alerts doctors to patients who need to be referred to specialists for more in-depth testing, diagnoses and treatments.

AMDx is currently testing its system in a handful of clinics in Mississippi and Tennessee. Training doctors is easy, Chaum says, because the cameras have features like auto-focus, and they don't require that the patients' eyes be dilated. The photos are sent over the Internet to AMDx's servers and automatically compared to images in its database. Chaum then checks each result manually--a process that takes about 90 seconds per case, he says.

AMDx's goal is to ultimately turn over the entire job to its computers, but for now it must rely on Chaum's review. That's because some insurers--most notably Medicare and Medicaid--will only reimburse physicians for eye screenings after an ophthalmologist examines the results. Chaum and Tobin are collecting data, with the goal of proving to both regulators and insurers that their computers are as effective as Chaum at detecting disease. "The computers can handle thousands of reports a day. The bottleneck is me signing off on them," Chaum admits.

Efforts to remotely diagnose eye diseases have been tried on a limited basis by the Veterans Administration hospitals and other institutions. Some ophthalmologists believe that if the idea catches on, they'll be able to treat many more cases of diabetic retinopathy than they can today. "If you can teach a clinician to recognize changes in the eye, you can teach a computer to do it," says Barrett Katz, an ophthalmologist and professor at Montefiore Medical Center and the Albert Einstein College of Medicine in the Bronx, NY. "It doesn't take an ophthalmologist to gather these images--if anyone could do it, that would be a major step forward."

Tobin and Chaum are on the hunt for venture capital to fund AMDx's expansion. They hope the current focus on health reform will give them a boost, because the ongoing debate is drawing attention to the need for improving the efficiency of the health care system. "It's not that we can't treat diabetic retinopathy," Chaum says. "It's that we're inefficient in how we screen for it."

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brennanj

2 Comments

  • 642 Days Ago
  • 05/14/2010

Congratulations

Congratulations on a great article.  I explore this and several other trends in The Future of Analytics (http://www.futureofanalytics.com)

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erbium

340 Comments

  • 638 Days Ago
  • 05/18/2010

Similar to EyePACS

While interning with an opthalmology practice I researched this:

EyePACS

a project of UC SF and UC berkeley,
a web based system to communicate digital photos of patients eyes - retinal detail from clinics.

https://www.eyepacs.org/RelatedResources/EyePACS_FrequentlyAskedQuestions.pdf

The system is designed to take digital photographs by non doctor personnel in a clinic and transmit them for analysis to increase compliance of yearly diabetic screening.

The only difference is that this is the automated portion for the exam itself that is not in the EyePACS system.  The review on the other side of the web is not automated.

Maybe they should get together.

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