The ERA OF ARTIFICIAL INTELLIGENCE

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Artificial Intelligence

Written By: Linda Roach, Contributing Writer

AI is poised to revolutionize medicine. An overview of the field, with selected applications in ophthalmology. https://www.aao.org/eyenet/article/artificial-intelligence


From the back of the eye to the front, artificial intelligence (AI) is expected to give ophthalmologists new automated tools for diagnosing and treating ocular diseases.. But, in ophthalmic AI circles, computerized analytics are being viewed as the path toward more efficient and more objective ways to interpret the flood of images that modern eye care practices produce, according to ophthalmologists involved in these efforts.

Khanna Institute Of Lasik
Khanna Institute Of Lasik

Starting With Retina

The most immediately promising computer algorithms are in the field of retinal diseases. For instance, researchers from the Google Brain initiative reported in 2016 that their “deep learning” AI system had taught itself to accurately detect diabetic retinopathy (DR) and diabetic macular edema in fundus photographs.1

And AI is being applied to other retinal conditions, notably including age-related macular degeneration (AMD),2,3 retinopathy of prematurity (ROP),4 and reticular pseudodrusen.5

No More Cataracts After Seeing Dr. Khanna, Los Angeles Ophthalmologist

 Researchers are developing AI-based systems to better detect or evaluate other ophthalmic conditions, including pediatric cataract,6 glaucoma,7 keratoconus,8 corneal ectasia,9 and oculoplastic reconstruction.10

“There’s a whole spectrum, all the way from screening to full management, where these algorithms can make things better and make things more objective. And an AI system gives the same answer every time,” 

3D OCT. Dr. Schmidt-Erfurth’s group developed a fully automated segmentation algorithm for the posterior vitreous boundary. These images are of the boundary in patients with (left panel) and without (right panel) vitreomacular adhesion.

Where Do MDs Fit In?

One of the most common concerns clinical ophthalmologists and other physicians express about AI is that it will replace them but AI as just another tool in their diagnostic armamentarium.

High-powered software. When these tools are ready for widespread clinical use, physicians won’t need to become AI experts, because the software is likeliest to reside within devices like optical coherence tomography (OCT) machines, 

“An automated algorithm is just a software tool, and ours are all based on routine OCT images—[using] the same OCT machines that are available in thousands and thousands of hospitals and private offices,” 

AUTOMATED ASSESSMENT. Unsupervised clustering of vitreomacular interface configurations in patients with (top panel) branch retinal vein occlusion and (bottom panel) central RVO.

AI Basics

Although the term artificial intelligence originated in the 1950s, the concept was still languishing on the fringes of computer science as recently as two decades ago, Dr. Abràmoff said. He and others wanted to try to echo the human brain’s mechanisms with “neural networks,”.

Today, there are a variety of approaches to building AI systems to automatically detect and measure pathologic features in images of the eye. The labels are sometimes used interchangeably; all of them in some way analyze pixels and groups of pixels in fundus photographs, or 3-dimensional “voxels” in OCT images.

Simple automated detectors. In the simplest form of AI, programmers give the software mathematical descriptors of the features to detect, and a rules-based algorithm looks for these patterns on incoming images (“pattern recognition”). Positive “hits” are combined to produce a diagnostic indicator.

Basic machine learning. In this approach, the algorithm is given some basic rules about what disease features look like, along with a “training set” of images from affected and unaffected eyes. The algorithms examine the images to learn about the differences.

Advanced machine learning. This type of machine learning structure consists of one or two interconnected layers of small computing units called “neurons,” which mimic the multilayered structure of the visual cortex.

Deep learning with convolutional neural networks (CNNs). The term “deep learning” is used because there are multiple interconnected layers of neurons—and because they require new approaches to train them. This latest iteration of AI comes closer to resembling “thinking,” because CNNs learn to perform their tasks through repetition and self-correction.

Disease feature–based versus image-based (“black box”) learning. Many ophthalmic AI researchers prefer to design their machine learning algorithms based on clinically known characteristics of disease, such as hemorrhages or exudates. So, when a supervised learning algorithm works, scientists can verify that its output is based on the presence of the same image characteristics that a human would identify, and they can adjust the algorithm if necessary.



Research Spotlight: DR

Computerized algorithms for detection and management of DR are the main focus for many teams of ophthalmologists, computer scientists, and mathematicians around the world.

Research Spotlight: AMD

In Austria, Dr. Schmidt-Erfurth assembled a computational imaging research team for pragmatic reasons. Expert OCT graders at her department’s Vienna Reading Center were being overwhelmed by the task of manually grading images from a series of large international clinical trials of anti-AMD drugs, she said.

True breakthroughs? Their efforts have produced deep learning algorithms that she believes constitute “true breakthroughs” in the evaluation and treatment of eyes with AMD.

Monitoring therapy’s effectiveness. “First, we have developed algorithms that can not only recognize disease activity on OCT scans but also can assess this activity2 precisely,” Dr. Schmidt-Erfurth said. “Each time we see a patient we can say ‘yes’ or ‘no’ [that] there is fluid there. We also can quantify the amount of fluid, to determine whether the disease is more active or less active than before. This is unique in AMD.”

Predictions based on drusen. The second breakthrough is an AI algorithm for making individualized predictions about eyes with drusen underneath the retina, the earliest stage of AMD, Dr. Schmidt-Erfurth said. The algorithm does this by quantifying drusen volume on OCT and tracking how the volume changes over time.12

“Our algorithm can predict the course of the disease. It can identify exactly which patients will develop which type of advanced disease, whether it may be wet or dry [AMD], and it allows us to identify at-risk patients just by using the noninvasive in vivo imaging. You can do it precisely for each individual, and it’s all based on routine OCTs,” she said.

Individualizing treatment intervals. Moreover, the group has developed an algorithm that can make an individualized prediction of recurrence intervals after intravitreal injection therapy for neovascular AMD.3 This information can help physicians avoid over- or undertreatment when the therapy is provided on an as-needed basis. The algorithm bases its predictions on the subretinal fluid volume in the central 3 mm during the first 2 months after therapy initiation, and it has an accuracy of 70% to 80%.3

Next up: A hunt for new biomarkers. Dr. Schmidt-Erfurth said that, as scientists refine and study their deep learning algorithms, she expects that her group’s algorithms will discover new, previously unsuspected biomarkers that will help ophthalmologists treat patients. This is because deep learning systems notice details that are not readily apparent to the human eye, she said.

“These algorithms are not limited to what we as traditional clinicians believe is a pathological feature,” she said. “They are searching by themselves and identifying entirely new biomarkers. And this unsupervised learning will really help us to understand disease beyond the already conventional knowledge.”



Current Limitations

The groundswell of research interest in AI can’t mask the fact that the field is grappling with some significant challenges.

Quality of the training sets. If the training set of images given to the AI tool is weak, the software is unlikely to produce accurate outcomes. “The systems are only as good as what they’re told. It’s important to come up with robust reference standards,” Dr. Chiang said.

Problems with image quality. “The state-of-the-art systems are very good at finding diabetic eye disease. But one thing they’re not very good at recognizing is when they’re not seeing diabetic eye disease. For example, these systems will often get confused by a patient who has a central retinal vein occlusion instead of diabetic retinopathy,” Dr. Chiang said.

He added, “Another challenge is that a certain percentage of images aren’t very good. They’re blurry or don’t capture enough of the retina. It’s really important to make sure that these systems recognize when images are of inadequate quality.”

The black box dilemma. When a CNN-based system analyzes a new image or data, it does so base upon its own self-generated rules. How, then, can the physician using a deep learning algorithm really know that the outcome is correct? This is the “black box” problem that haunts some medical AI researchers and is downplayed by others, Dr. Abràmoff said.

Wrong answers. Dr. Abràmoff concocted an experiment that he believes illustrates why there is reason for concern. His team changed a small number of pixels in fundus photographs of eyes with DR and then gave these “adversarial” images to image-based black box CNN systems for evaluation. The changes in the images were minor, undetectable to an ophthalmologist’s eye. However, when these CNNs evaluated the altered images, more than half the time they judged them to be disease-free, Dr. Abràmoff said.13

Looking Ahead

Despite these challenges, it’s clear that AI will occupy an increasingly critical role in medicine.

A valuable research tool. “There is definitely a huge role for neural networks in research, for hypothesis generation and discovery,” Dr. Abràmoff said. “For instance, to find out whether associations exist between some retinal disease and some image feature, such as in hypertension. There, it does not matter initially that the neural network cannot be fully explained. Once we know an association exists, we then explore what the nature of that association is.”

Augmentation, not replacement, of MDs. Dr. Chiang, who is helping to develop AI techniques to assess ROP, said that he believes automated systems can and should complement what physicians do.

“Machines can help the doctor make a better diagnosis, but they are not good at making medical decisions afterward,” he said. “Doctors and patients make management decisions by working together to weigh the various risks and benefits and treatment alternatives. The role of the doctor will continue to [involve] the art of medicine—which is a uniquely human process.”

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From the back of the eye to the front, artificial intelligence (AI) is expected to give ophthalmologists new automated tools for diagnosing and treating ocular diseases. AI systems are already available or in development for the detection of multiple ophthalmic diseases, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. But, in ophthalmic AI circles, computerized analytics are being viewed as the path toward more efficient and more objective ways to interpret the flood of images that modern eye care practices produce, according to ophthalmologists involved in these efforts.

The numbers are staggering. About 8 million people in the United States have an early, often asymptomatic, stage of age-related macular degeneration (AMD), a leading cause of blindness in those over 50.

“AMD requires careful monitoring by an ophthalmologist, but we estimate only about 4 million of those in this asymptomatic stage even know they have it,” says Neil Bressler, M.D., Wilmer’s James P. Gills Professor of Ophthalmology. “We—ophthalmologists— can’t look in each person’s eyes to find who has this intermediate stage that needs monitoring because we would work all day, all night,” he says

The earliest forms of medical AI were simple automated detectors, designed to recognize a defined set of disease features that were programmed into the system. A limitation of these early systems is that they will recognize only patients who express features that are included in the defined program. The most advanced iteration of medical AI teaches itself the features of disease by analysing a representative set of images from people with and without the disease, and possibly across various stages of disease. During the learning phase, the system performs multiple rounds of analysis, assessment, and re-analysis until each image can be faithfully identified. In contrast to simple automated detectors, AI systems that self-teach (called deep learning with convolutional neural networks) are unconstrained in the number of disease features that they may identify.1

AI in practice and development 

Medical AI systems are now available or in development for the detection of a number of ophthalmic diseases, including DR, wet AMD, cataract, and glaucoma. AI systems are tested for their ability to accurately detect a disease, and this is typically assessed with measures called sensitivity and specificity. Sensitivity is a measure of how well the system catches all positive cases of the disease, and specificity is a measure of how well the system avoids false positives. For each measure, the higher the value (on a scale of 0.0% to 100%), the better the accuracy.

The most developed AI systems in ophthalmology are those that are designed to detect Diabetic Retinopathy. Google Brain is a deep learning AI research team that created a system to identify patients with DR and DME based solely on the analysis of retina fundus photos. The accuracy of Google Brain’s AI system was evaluated with two test runs that use fundus photos from patients that had already been diagnosed by expert physicians (The EyePACS-1 data set and Messidor-2 data set). Depending on how the analysis was performed (whether focused on sensitivity or specificity), Google Brain’s AI system had sensitivity values of 97.5% and 96.1% in each practice set, and specificity values of 98.1% and 98.5%.2

IDx is an AI company working on separate deep learning systems for the detection of multiple ophthalmic diseases, including DR, AMD, and glaucoma. The IDx-DR system is designed for use in a primary care setting, and provides results within a minute of submitting fundus photos. Any patient with a positive diagnosis of DR receives a corresponding referral to an ophthalmologist.

Deep learning AI approaches are also in development that could improve the care of patients with wet AMD. These systems are being used to identify anatomic OCT-based features that could predict the timing and extent of disease progression, or which patients will require extensive anti-VEGF treatment after the initiation phase.6,7 Another deep learning AI system has been shown to accurately characterize the pattern of intraretinal fluid in patients with wet AMD or retinal vein occlusion (RVO), with the ability to localize, quantify, and distinguish between intraretinal cysts and subretinal fluid.8

AI systems are being developed and validated for the automatic diagnosis and characterization of other ophthalmic diseases, beyond those that affect the retina, including the following conditions that affect the anterior segment: AI to diagnose and grade (location, density, and opacity) cataract in pediatric patients, based on an analysis of slit-lamp images9; AI to diagnose glaucoma in adolescent or adult patients, based on measurement of the visual field and thickness of the retinal nerve fiber layer (on OCT)10; AI to diagnose keratoconus, based on Scheimpflug tonometry that provides measures of corneal curvature, thickness, opacities.11

An especially exciting development in the field of ophthalmology AI came with the report of a system developed as part of a collaboration between Moorfields Eye Hospital in London and another Google AI team, DeepMind. These teams created an AI system that combines two DLS with the ability to detect 50 ophthalmic diseases based on analysis of three-dimensional OCT data. The first DLS uses the raw OCT data to create a tissue map, and then the second DLS analyzes the tissue map for potential markers of disease.12,13

The DeepMind system was validated in a study that showed it was 94% sensitive, catching most positive cases of each disease. In fact, DeepMind performed as well or better than human clinical experts (retina specialists and optometrists with medical retina training), depending on who the experts were and how much additional information they had to work with (e.g. fundus images, patient medical histories). What’s also impressive is that the system gives more than just a yes-or-no diagnosis, but provides multiple levels of actionable information. For instance, the system provides probabilities for multiple similar diseases in addition to the top pick. The system also provides an accompanying recommendation on urgency of referral, with options of ‘observation only’, ‘routine’, ‘semi-urgent’, and ‘urgent’.12,13

Perhaps what’s most intriguing though, is that the system gives insight into how the diagnosis was made. Until now, most DLS systems have operated within a ‘black box’, where the images are loaded and the answer comes out the other end, and beyond validation studies, you have to trust the result. In contrast, the DeepMind system is giving information along the path, for those who need to see the inner workings, almost like a proof in math class.12,13   

Increasing Access

Despite these challenges, it’s clear that AI will occupy an increasingly critical role in medicine.

Channa and Ingrid E Zimmer-Galler, M.D., a Wilmer retinal specialist, have partnered with Risa Wolf, M.D., an endocrinologist in the Johns Hopkins Children’s Center, to use the first FDA-cleared AI screening device on pediatric patients with diabetes. Currently, the device is cleared by the FDA for adults, so part of their study focuses on testing its performance in children. Another part of their study aims to examine how effective an AI screening device is in an endocrinologist’s clinic. Because all people with diabetes see their endocrinologist, Channa’s team supposes that an AI screening device in that office will increase compliance with the recommended annual eye screening for patients with diabetes.

Adrienne Scott, M.D., a retinal specialist at Wilmer, is pursuing a similar strategy to help patients with sickle cell retinopathy, her area of research. She and third-year resident Sophie Cai, M.D., are exploring whether they can build a deep learning algorithm that recognizes the retinal signs of sight-threatening sickle cell retinopathy.

Looking Ahead

A valuable research tool. “There is definitely a huge role for neural networks in research, for hypothesis generation and discovery,” Dr. Abràmoff said. “For instance, to find out whether associations exist between some retinal disease and some image feature, such as in hypertension. There, it does not matter initially that the neural network cannot be fully explained. Once we know an association exists, we then explore what the nature of that association is.”

Augmentation, not replacement, of MDs. Dr. Chiang, who is helping to develop AI techniques to assess ROP, said that he believes automated systems can and should complement what physicians do.

“Machines can help the doctor make a better diagnosis, but they are not good at making medical decisions afterward,” he said. “Doctors and patients make management decisions by working together to weigh the various risks and benefits and treatment alternatives. The role of the doctor will continue to [involve] the art of medicine—which is a uniquely human process.”

Conclusion:=

As artificial Intelligence (AI) is becoming much more common for screening, detecting and also helping deal with eye problems. The technology already is used in online search engines, speech acknowledgment tools as well as various other smart gadgets. Currently, AI is revealing its signature in healthcare.

Massive quantities of data as well as expanding calculating power are sustaining these advanced, algorithm-based modern technologies.

A number of researches show that there is capacity for AI to aid doctors detect eye illness. Yet further study is needed to confirm the technologies do what they set out to. It will take a while for eye doctors to depend on and also use AI-based tools in their methods.

The ERA OF ARTIFICIAL INTELLIGENCE

The numbers are staggering. About 8 million people in the United States have an early, often asymptomatic, stage of age-related macular degeneration (AMD), a leading cause of blindness in those over 50.

“AMD requires careful monitoring by an ophthalmologist, but we estimate only about 4 million of those in this asymptomatic stage even know they have it,” says Neil Bressler, M.D., Wilmer’s James P. Gills Professor of Ophthalmology. “We—ophthalmologists— can’t look in each person’s eyes to find who has this intermediate stage that needs monitoring because we would work all day, all night,” he says.

But what if computers could be trained to do the job instead? Suppose they could even teach themselves an algorithm to identify the signs of AMD—areas of debris that have accumulated behind the retina, called drusen—on photos of retinas?

This “deep learning” approach was precisely the strategy that Bressler and a team of computer scientists at the Johns Hopkins University Applied Physics Lab (APL), with whom he’s been working for more than 10 years, undertook recently in an important study aimed at expanding access to eye care by tapping into the power of technology.

Their work is just one of a dozen projects currently underway at Wilmer that are harnessing advances in artificial intelligence to improve clinical care in ophthalmology. In glaucoma, Jithin Yohannan, M.D., is using deep learning, as well as other machine learning approaches, to predict the likelihood of disease progression and ultimately improve patient outcomes. While in the cornea division, Albert Jun, M.D., Ph.D., the chief of the division and the Walter J. Stark, M.D. Professor of Ophthalmology, is devising an algorithm to more accurately select intraocular lenses for patients undergoing cataract surgery, and Shameema Sikder, M.D., director of the Center of Excellence for Ophthalmic Surgical Education and Training, is creating new, more effective methods for improving cataract surgery skill.

“What’s becoming clear is that harnessing this new technology holds the promise of transforming much of ophthalmology, and this is now a Wilmer-wide effort,” says Wilmer Director Peter J. McDonnell, M.D. “Computer science can perform some tasks extremely well, freeing up ophthalmologists to focus their time and energy on only those tasks that require all their years of training and that call for ‘the human touch.’”

The Future Is Now

The concept of neural networks, a basis for deep learning, has been around since the 1940s and is modeled on how the brain works. Sikder describes neural networks as producing “smart, iterative learning.” Neural networks begin with an algorithm, which ingests data and learns something about that data that can be applied to assess something else, she says.

“It [the algorithm] learns it once and then looks at it slightly differently, then learns it again and looks at it in multiple different ways to really get a full picture,” she says. “Like if you take an elephant and first, you touch the ears, and then you touch the tail, and then you touch the skin, and over time, if you feel enough parts, you’ll actually have an assessment that it really is an elephant in the room.”

To run programs, computers use central processing units, which are good at tackling calculations in sequential order. In order to run all of the iterations required for deep learning to succeed, a lot of hardware is required. And that’s expensive. A little less than a decade ago, however, researchers began to use graphical processing units, GPUs, which had been popularized for video games, to “supercharge” computing. They perform tasks in parallel—vastly increasing the iterations an algorithm can perform, for a lot less money.

The final piece of the puzzle that has launched deep learning into everyday conversation is the availability of data — a lot of data. The most useful data are that which are already labeled, because the algorithm needs labeled data to learn. Because of their roots in the video game universe, GPUs are good at rendering images. GPUs paired with deep learning algorithms are especially good at analyzing medical images.

A Picture Is Worth…

It was precisely the plethora of images available to Bressler and the ophthalmology research community that drew the interest of APL researchers to his work in macular degeneration.

The deep learning technique Bressler’s team used is “supervised learning.” To begin this process, a research team decides what it wants the neural network algorithm to learn to recognize based on the type of labeled data available. Then the team members gather the data, which they divide into a training dataset and a testing dataset.

For Bressler’s project, researchers chose the presence of drusen on fundus images taken of retinas. Next, they created a neural network algorithm to read the images. For each image, the algorithm is told the “ground truth.” Each time Bressler’s team fed an image to their neural network, they would provide “the ground truth” by stating, “this image has drusen” or “this image does not have drusen.” After viewing thousands of images paired with the ground truth, the neural network learned to sort the images into the two categories with increased accuracy.

Once the neural network is trained, researchers feed it testing data in order to validate the neural network’s accuracy. The neural network algorithm Bressler’s team created accurately identified AMD between 88.4 percent and 91.6 percent of the time, which is comparable to the accuracy achieved by human experts. They published the results in JAMA Ophthalmology in 2016.

“Deep learning artificial intelligence tools represent a new generation of screening for eye diseases that require less time and skilled personnel, while generating expert evaluations as if a patient were being diagnosed by one of the top clinicians at Wilmer,” says Bressler. “These AI tools hold the promise to widen access to Wilmer’s ophthalmic care and to detect and treat these retinal diseases before substantial vision loss has occurred, when treatments usually are most effective.”

Increasing Access

Many of Wilmer’s deep learning AI projects focus on increasing access to diagnostic care for conditions that are asymptomatic. People with systemic diseases, such as diabetes and sickle cell disease, are at risk for retinopathy. While they are usually referred for annual eye exams, many don’t follow up because they are not experiencing eye problems or have too many other medical appointments to juggle.

“Patients with diabetes [often] have a lot going on in their lives,” says Roomasa Channa, M.D., Wilmer’s chief resident for 2017–2018. “We need to make detection of eye problems easier for these patients.”

Channa and Ingrid E Zimmer-Galler, M.D., a Wilmer retinal specialist, have partnered with Risa Wolf, M.D., an endocrinologist in the Johns Hopkins Children’s Center, to use the first FDA-cleared AI screening device on pediatric patients with diabetes. Currently, the device is cleared by the FDA for adults, so part of their study focuses on testing its performance in children. Another part of their study aims to examine how effective an AI screening device is in an endocrinologist’s clinic. Because all people with diabetes see their endocrinologist, Channa’s team supposes that an AI screening device in that office will increase compliance with the recommended annual eye screening for patients with diabetes.

Adrienne Scott, M.D., a retinal specialist at Wilmer, is pursuing a similar strategy to help patients with sickle cell retinopathy, her area of research. She and third-year resident Sophie Cai, M.D., are exploring whether they can build a deep learning algorithm that recognizes the retinal signs of sight-threatening sickle cell retinopathy.

Since sickle cell retinopathy is rarer than AMD and diabetic retinopathy, the researchers have fewer images available to train a neural network. “We want to see if, with one of these rarer diseases with a smaller dataset, we can still extract information to be able to develop an algorithm,” says Cai.

Recognizing the challenges ahead, Scott remains undaunted. “Sickle cell is a disease that needs increased access to this type of care and to streamlined care in general for the sake of patient convenience and cost,” she says.

The Power of Prediction

Since deep learning has the capacity to improve patient outcomes because of its predictive power, researchers are using data to train the algorithms to spot patterns humans cannot. “As humans, we know how to classify or identify glaucoma or classify AMD. But if we can predict what’s going to happen in [a patient’s] future based on how things look today, that’ll be really powerful,” says Yohannan, Wilmer’s current chief resident. One of his research projects aims to do that for glaucoma patients.

When patients present with glaucoma, they take a visual field test that assesses their peripheral vision using 52 points of data. The better their peripheral vision is, the less severe the glaucoma. As patients continue with treatment, they continue to take visual field tests so doctors can track the progression of the disease.

While some patients get treatment and level off, others get treatment and rapidly decline.

Using a large database of about 210,000 visual field tests, Yohannan has constructed both deep learning and other machine learning algorithms to predict which patients are at risk for rapidly progressing glaucoma based on their very first field test. Thus far, he is pleased with the results.


“If you did this by chance alone, it would be about 50/50—it can be one category or the other. But just on this dataset, we’re about able to get 90 percent accuracy. So correctly classify nine-tenths of people,” says Yohannan.

“In the future, it would be valuable if a patient comes in to get their first field test and we can tell them, you’re at high risk for progressing, and we want to keep a close eye on you. Maybe those are the patients we want to see every two or three months rather than every six months,” says Yohannan.

Jun and his research partner, John Ladas, M.D., Ph.D., are also exploiting the awesome pattern recognition ability of neural networks. Their deep learning algorithm predicts how an eye will heal after cataract surgery. This will allow surgeons to choose the most accurate focusing power for the artificial lens the patient will receive during the surgery.

With Great Power Comes Great Responsibility

Patients can take heart that Wilmer researchers are approaching these issues with eyes open. Optimism is great, but so is verification.

“There is a lot of buzz in the media about health care and AI. Like everything new, it will follow a curve of enthusiasm followed by skepticism before we reach a balance where we really understand its role in patient care,” says Channa. “I think we need more research into how implementing a particular AI algorithm helps the patient and the health care system.”

Fortunately, Wilmer specializes in research aimed at connecting discoveries to patient care.

ARTICLE 

ARTIFICIAL INTELLIGENCE TRENDS IN EYE CARE

Thinking cyborg. Close up - Royalty-free Artificial Intelligence Stock Photo

Artificial Intelligence (AI) is becoming much more common for screening, detecting and also helping deal with eye problems. The technology already is in use online search engines, speech acknowledgment tools as well as various other smart gadgets. Currently, AI is revealing its signature in healthcare.

Massive quantities of data as well as expanding calculating power are sustaining these advanced, algorithm-based modern technologies.

A number of researches show that there is capacity for AI to aid doctors detect eye illness. Yet further study is needed to confirm the technologies do what they set out to. It will take a while for eye doctors to depend on and also use AI-based tools in their methods.

Artificial Intelligence excels at Picture Recognition.
AI has actually been growing in popularity where photo evaluation is essential to help in medical diagnosis and also treatment. Specialties such as radiology, pathology, dermatology and also ophthalmology are amongst those leading the study of where AI can be used effectively.

AI-based systems – often described as deep-learning computer systems – are educated with numerous photos of the eye. The algorithms learn the distinction in between typical picture and also unusual pictures. Researchers already have actually tested AI-based systems that utilize:

  • photos of the retina to acknowledge people in danger for cardiovascular disease, and x-ray photos to aid recognize paediatric pneumonia.
  • AI may also be able to aid discovery of diabetic person retinopathy as well as macular deterioration. It’s approximated that 61 million grown-ups in the USA are at high risk for vision loss. Only fifty percent of them consulted an ophthalmologist in the past one year. New modern technologies can get even more people to an eye doctor sooner for medical diagnosis and therapy, if they immediately make initial screenings more convenient and also accessible.  Further, this can proceed for a very early treatment and through which we can protect against or decrease vision loss. Where healthcare is hard to discover, AI-based systems can assist individuals get there, that otherwise may go without treatment.

    “AI won’t change medical professionals, however it will certainly make them even more efficient,” claims Rahul N. Khurana, MD, an eye doctor. “It can assist us find and also see even more of the people that may be failing the cracks of the healthcare system.”

    Cameras to Identify Diabetic Retinopathy


Greater than 29 million Americans have diabetes, a very high number. People with diabetes are at risk for diabetic retinopathy – a potentially blinding eye condition which develops as a result of diabetes. But people don’t typically discover adjustments in their vision in the disease’s onset. Worldwide, there are insufficient ophthalmologists to care for all the people that need diabetic eye testing.

That’s where innovation like the IDx-DR can help. The IDx-DR is the first FDA-approved AI-based tool for discovering diabetic retinopathy. Primary care doctors and also various other doctors can utilize AI. IDx-DR examines pictures of the eye taken with a retinal video camera. The software program informs the medical professional if a patient ought to see an eye doctor for feasible therapy.

And also, a March 2018 research released in Ophthalmology, a journal of the American Academy of Ophthalmology, discovered that disease-detecting AI software can precisely determine early warning signs of diabetic retinopathy. This study gives a solid foundation for future researches.

Software Program to Identify Macular Degeneration
Macular degeneration triggers main vision loss as well as is frequently not visible until vision is extremely blurry. The condition affects more than 10 million Americans. A February 2018 study in Cell Publication revealed that man-made intelligence-based software programs can recognize very early indications of macular deterioration. To do this, the software was taught making use of pictures picked by experts in picture evaluation. Focusing on these pictures that provide clear diagnostic examples permitted the software to gain from a smaller sized variety of photos and generate outcomes quicker. This technology may be able to aid physicians in getting more people into treatment earlier.

AI likewise could aid ophthalmologists map and measure exactly how a person’s disease is progressing. Computer systems can also speedily refine multiple eye scans as well as sets of details. AI might one day aid eye doctors in therapy choices for problems like macular deterioration, glaucoma as well as more diseases.

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