AMD
Artificial Intelligence Getting Smarter! Innovations from the Vision Field
Posted on by Michael F. Chiang, M.D., National Eye Institute
One of many health risks premature infants face is retinopathy of prematurity (ROP), a leading cause of childhood blindness worldwide. ROP causes abnormal blood vessel growth in the light-sensing eye tissue called the retina. Left untreated, ROP can lead to lead to scarring, retinal detachment, and blindness. It’s the disease that caused singer and songwriter Stevie Wonder to lose his vision.
Now, effective treatments are available—if the disease is diagnosed early and accurately. Advancements in neonatal care have led to the survival of extremely premature infants, who are at highest risk for severe ROP. Despite major advancements in diagnosis and treatment, tragically, about 600 infants in the U.S. still go blind each year from ROP. This disease is difficult to diagnose and manage, even for the most experienced ophthalmologists. And the challenges are much worse in remote corners of the world that have limited access to ophthalmic and neonatal care.
Artificial intelligence (AI) is helping bridge these gaps. Prior to my tenure as National Eye Institute (NEI) director, I helped develop a system called i-ROP Deep Learning (i-ROP DL), which automates the identification of ROP. In essence, we trained a computer to identify subtle abnormalities in retinal blood vessels from thousands of images of premature infant retinas. Strikingly, the i-ROP DL artificial intelligence system outperformed even international ROP experts [1]. This has enormous potential to improve the quality and delivery of eye care to premature infants worldwide.
Of course, the promise of medical artificial intelligence extends far beyond ROP. In 2018, the FDA approved the first autonomous AI-based diagnostic tool in any field of medicine [2]. Called IDx-DR, the system streamlines screening for diabetic retinopathy (DR), and its results require no interpretation by a doctor. DR occurs when blood vessels in the retina grow irregularly, bleed, and potentially cause blindness. About 34 million people in the U.S. have diabetes, and each is at risk for DR.
As with ROP, early diagnosis and intervention is crucial to preventing vision loss to DR. The American Diabetes Association recommends people with diabetes see an eye care provider annually to have their retinas examined for signs of DR. Yet fewer than 50 percent of Americans with diabetes receive these annual eye exams.
The IDx-DR system was conceived by Michael Abramoff, an ophthalmologist and AI expert at the University of Iowa, Iowa City. With NEI funding, Abramoff used deep learning to design a system for use in a primary-care medical setting. A technician with minimal ophthalmology training can use the IDx-DR system to scan a patient’s retinas and get results indicating whether a patient should be sent to an eye specialist for follow-up evaluation or to return for another scan in 12 months.
Many other methodological innovations in AI have occurred in ophthalmology. That’s because imaging is so crucial to disease diagnosis and clinical outcome data are so readily available. As a result, AI-based diagnostic systems are in development for many other eye diseases, including cataract, age-related macular degeneration (AMD), and glaucoma.
Rapid advances in AI are occurring in other medical fields, such as radiology, cardiology, and dermatology. But disease diagnosis is just one of many applications for AI. Neurobiologists are using AI to answer questions about retinal and brain circuitry, disease modeling, microsurgical devices, and drug discovery.
If it sounds too good to be true, it may be. There’s a lot of work that remains to be done. Significant challenges to AI utilization in science and medicine persist. For example, researchers from the University of Washington, Seattle, last year tested seven AI-based screening algorithms that were designed to detect DR. They found under real-world conditions that only one outperformed human screeners [3]. A key problem is these AI algorithms need to be trained with more diverse images and data, including a wider range of races, ethnicities, and populations—as well as different types of cameras.
How do we address these gaps in knowledge? We’ll need larger datasets, a collaborative culture of sharing data and software libraries, broader validation studies, and algorithms to address health inequities and to avoid bias. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) project and NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program project will be major steps toward addressing those gaps.
So, yes—AI is getting smarter. But harnessing its full power will rely on scientists and clinicians getting smarter, too.
References:
[1] Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.
[2] FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Food and Drug Administration. April 11, 2018.
[3] Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Diabetes Care. 2021 May;44(5):1168-1175.
Links:
Retinopathy of Prematurity (National Eye Institute/NIH)
Diabetic Eye Disease (NEI)
Michael Abramoff (University of Iowa, Iowa City)
Bridge to Artificial Intelligence (Common Fund/NIH)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the second in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]
Finding Better Ways to Image the Retina
Posted on by Dr. Francis Collins
Every day, all around the world, eye care professionals are busy performing dilated eye exams. By looking through a patient’s widened pupil, they can view the retina—the postage stamp-sized tissue lining the back of the inner eye—and look for irregularities that may signal the development of vision loss.
The great news is that, thanks to research, retinal imaging just keeps getting better and better. The images above, which show the same cells viewed with two different microscopic techniques, provide good examples of how tweaking existing approaches can significantly improve our ability to visualize the retina’s two types of light-sensitive neurons: rod and cone cells.
Specifically, these images show an area of the outer retina, which is the part of the tissue that’s observed during a dilated eye exam. Thanks to colorization and other techniques, a viewer can readily distinguish between the light-sensing, color-detecting cone cells (orange) and the much smaller, lowlight-sensing rod cells (blue).
These high-res images come from Johnny Tam, a researcher with NIH’s National Eye Institute. Working with Alfredo Dubra, Stanford University, Palo Alto, CA, Tam and his team figured out how to limit light distortion of the rod cells. The key was illuminating the eye using less light, provided as a halo instead of the usual solid, circular beam.
But the researchers’ solution hit a temporary snag when the halo reflected from the rods and cones created another undesirable ring of light. To block it out, Tam’s team introduced a tiny pinhole, called a sub-Airy disk. Along with use of adaptive optics technology [1] to correct for other distortions of light, the scientists were excited to see such a clear view of individual rods and cones. They published their findings recently in the journal Optica [2]
The resolution produced using these techniques is so much improved (33 percent better than with current methods) that it’s even possible to visualize the tiny inner segments of both rods and cones. In the cones, for example, these inner segments help direct light coming into the eye to other, photosensitive parts that absorb single photons of light. The light is then converted into electrical signals that stream to the brain’s visual centers in the occipital cortex, which makes it possible for us to experience vision.
Tam and team are currently working with physician-scientists in the NIH Clinical Center to image the retinas of people with a variety of retinal diseases, including age-related macular degeneration (AMD), a leading cause of vision loss in older adults. These research studies are ongoing, but offer hopeful possibilities for safe and non-intrusive monitoring of individual rods and cones over time, as well as across disease types. That’s obviously good news for patients. Plus it will help scientists understand how a rod or cone cell stops working, as well as more precisely test the effects of gene therapy and other experimental treatments aimed at restoring vision.
References:
[1] Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope. Dubra A, Sulai Y, Norris JL, Cooper RF, Dubis AM, Williams DR, Carroll J. Biomed Opt Express. 2011 Jul 1;2(7):1864-76.
[1] In-vivo sub-diffraction adaptive optics imaging of photoreceptors in the human eye with annular pupil illumination and sub-Airy detection. Rongwen L, Aguilera N, Liu T, Liu J, Giannini JP, Li J, Bower AJ, Dubra A, Tam J. Optica 2021 8, 333-343. https://doi.org/10.1364/OPTICA.414206
Links:
Get a Dilated Eye Exam (National Eye Institute/NIH)
How the Eyes Work (NEI)
Eye Health Data and Statistics (NEI)
Tam Lab (NEI)
Dubra Lab (Stanford University, Palo Alto, CA)
NIH Support: National Eye Institute
The Amazing Brain: Making Up for Lost Vision
Posted on by Dr. Francis Collins
Recently, I’ve highlighted just a few of the many amazing advances coming out of the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative. And for our grand finale, I’d like to share a cool video that reveals how this revolutionary effort to map the human brain is opening up potential plans to help people with disabilities, such as vision loss, that were once unimaginable.
This video, produced by Jordi Chanovas and narrated by Stephen Macknik, State University of New York Downstate Health Sciences University, Brooklyn, outlines a new strategy aimed at restoring loss of central vision in people with age-related macular degeneration (AMD), a leading cause of vision loss among people age 50 and older. The researchers’ ultimate goal is to give such people the ability to see the faces of their loved ones or possibly even read again.
In the innovative approach you see here, neuroscientists aren’t even trying to repair the part of the eye destroyed by AMD: the light-sensitive retina. Instead, they are attempting to recreate the light-recording function of the retina within the brain itself.
How is that possible? Normally, the retina streams visual information continuously to the brain’s primary visual cortex, which receives the information and processes it into the vision that allows you to read these words. In folks with AMD-related vision loss, even though many cells in the center of the retina have stopped streaming, the primary visual cortex remains fully functional to receive and process visual information.
About five years ago, Macknik and his collaborator Susana Martinez-Conde, also at Downstate, wondered whether it might be possible to circumvent the eyes and stream an alternative source of visual information to the brain’s primary visual cortex, thereby restoring vision in people with AMD. They sketched out some possibilities and settled on an innovative system that they call OBServ.
Among the vital components of this experimental system are tiny, implantable neuro-prosthetic recording devices. Created in the Macknik and Martinez-Conde labs, this 1-centimeter device is powered by induction coils similar to those in the cochlear implants used to help people with profound hearing loss. The researchers propose to surgically implant two of these devices in the rear of the brain, where they will orchestrate the visual process.
For technical reasons, the restoration of central vision will likely be partial, with the window of vision spanning only about the size of one-third of an adult thumbnail held at arm’s length. But researchers think that would be enough central vision for people with AMD to regain some of their lost independence.
As demonstrated in this video from the BRAIN Initiative’s “Show Us Your Brain!” contest, here’s how researchers envision the system would ultimately work:
• A person with vision loss puts on a specially designed set of glasses. Each lens contains two cameras: one to record visual information in the person’s field of vision; the other to track that person’s eye movements enabled by residual peripheral vision.
• The eyeglass cameras wirelessly stream the visual information they have recorded to two neuro-prosthetic devices implanted in the rear of the brain.
• The neuro-prosthetic devices process and project this information onto a specific set of excitatory neurons in the brain’s hard-wired visual pathway. Researchers have previously used genetic engineering to turn these neurons into surrogate photoreceptor cells, which function much like those in the eye’s retina.
• The surrogate photoreceptor cells in the brain relay visual information to the primary visual cortex for processing.
• All the while, the neuro-prosthetic devices perform quality control of the visual signals, calibrating them to optimize their contrast and clarity.
While this might sound like the stuff of science-fiction (and this actual application still lies several years in the future), the OBServ project is now actually conceivable thanks to decades of advances in the fields of neuroscience, vision, bioengineering, and bioinformatics research. All this hard work has made the primary visual cortex, with its switchboard-like wiring system, among the brain’s best-understood regions.
OBServ also has implications that extend far beyond vision loss. This project provides hope that once other parts of the brain are fully mapped, it may be possible to design equally innovative systems to help make life easier for people with other disabilities and conditions.
Links:
Age-Related Macular Degeneration (National Eye Institute/NIH)
Macknik Lab (SUNY Downstate Health Sciences University, Brooklyn)
Martinez-Conde Laboratory (SUNY Downstate Health Sciences University)
Show Us Your Brain! (BRAIN Initiative/NIH)
Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)
NIH Support: BRAIN Initiative
Regenerative Medicine: The Promise and Peril
Posted on by Dr. Francis Collins
Caption: Scanning electron micrograph of iPSC-derived retinal pigment epithelial cells growing on a nanofiber scaffold (blue).
Credit: Sheldon Miller, Arvydas Maminishkis, Robert Fariss, and Kapil Bharti, National Eye Institute/NIH
Stem cells derived from a person’s own body have the potential to replace tissue damaged by a wide array of diseases. Now, two reports published in the New England Journal of Medicine highlight the promise—and the peril—of this rapidly advancing area of regenerative medicine. Both groups took aim at the same disorder: age-related macular degeneration (AMD), a common, progressive form of vision loss. Unfortunately for several patients, the results couldn’t have been more different.
In the first case, researchers in Japan took cells from the skin of a female volunteer with AMD and used them to create induced pluripotent stem cells (iPSCs) in the lab. Those iPSCs were coaxed into differentiating into cells that closely resemble those found near the macula, a tiny area in the center of the eye’s retina that is damaged in AMD. The lab-grown tissue, made of retinal pigment epithelial cells, was then transplanted into one of the woman’s eyes. While there was hope that there might be actual visual improvement, the main goal of this first in human clinical research project was to assess safety. The patient’s vision remained stable in the treated eye, no adverse events occurred, and the transplanted cells remained viable for more than a year.
Exciting stuff, but, as the second report shows, it is imperative that all human tests of regenerative approaches be designed and carried out with the utmost care and scientific rigor. In that instance, three elderly women with AMD each paid $5,000 to a Florida clinic to be injected in both eyes with a slurry of cells, including stem cells isolated from their own abdominal fat. The sad result? All of the women suffered severe and irreversible vision loss that left them legally or, in one case, completely blind.
