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Ophthalmology/Optometry


Artificial Intelligence (AI) Discovers Precise Treatments for Blinding Diseases

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The team used artificial intelligence (AI) to analyze images of retinal pigment epithelium (RPE --a layer of retinal tissue that nourishes and supports the retina's light-sensing cells, photoreceptors) at single-cell resolution to create a reference map that locates each subpopulation within the eye. New Discovery on Eye Diseases Distinct differences among the retinal cells (tissue comprising the retina -- vital to human visual perception) have been now identified by the present study that sheds light on tissue targeted by age-related macular degeneration and other diseases. App Helps Parents Detect Signs of Eye Disorders in Children New App named CRADLE surpasses the'gold standard' of sensitivity in diagnosing the pediatric cancer Retinoblastoma, Baylor University researchers say. "These results provide a first-of-its-kind framework for understanding different RPE cell subpopulations and their vulnerability to retinal diseases, and for developing targeted therapies to treat them," says Michael F. Chiang, MD, NEI, National Institutes of Health. "The findings will help us develop more precise cell and gene therapies for specific degenerative eye diseases," says the study's lead investigator, Kapil Bharti, PhD, NEI.


AI and Novel Reference Map May Advance Retinal Disease Therapies

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Scientists from the National Eye Institute (NEI) discovered five subpopulations of retinal pigment epithelium (RPE). Using artificial intelligence (AI), the researchers were able to analyze images of RPE at single-cell resolution to create a reference map that locates each subpopulation within the eye. Their findings are published in the journal Proceedings of the National Academy of Sciences, in a paper titled, "Single-cell–resolution map of human retinal pigment epithelium helps discover subpopulations with differential disease sensitivity." "These results provide a first-of-its-kind framework for understanding different RPE cell subpopulations and their vulnerability to retinal diseases, and for developing targeted therapies to treat them," said Michael F. Chiang, MD, director of the NEI, part of the National Institutes of Health. "The findings will help us develop more precise cell and gene therapies for specific degenerative eye diseases," said the study's lead investigator, Kapil Bharti, PhD, who directs the NEI Ocular and Stem Cell Translational Research Section.


AOP experts discuss safe practice at 100% Optical

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AOP clinical and professional director, Dr Peter Hampson, and head of clinical negligence, Efa Schmidt, discussed safe practice and developments in artificial intelligence technology in their presentation The good, the bad and the ugly at 100% Optical (London ExCel, 23-25 April). Schmidt shared with the audience that her role involves overseeing clinical negligence claims at the AOP. "One of the issues that we find causes great concern amongst optometrists is the fear of litigation," she said. Schmidt added that sometimes optometrists can be fearful that their practice will be judged by a higher standard than is applied. She noted that optometrists are expected to act in a way that is reasonable, taking the same steps that the majority of other optometrists would take.


IIT-Jodhpur researchers come up with deep learning-based cataract detection method

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New Delhi, Apr 28 (PTI) Researchers at the Indian Institute of Technology (IIT) in Jodhpur have formulated a deep learning-based cataract detection method which is inexpensive and offers very high levels of accuracy. According to the research team, eye images acquired by low-cost near-infrared (NIR) cameras can aid in low-cost, easy-to-use and practical solutions for cataract detection. "The proposed multitask deep learning algorithm is inexpensive and results in very high levels of accuracy. This research presents a deep learning-based cataract detection method that involves iris segmentation and multitask network classification. The proposed segmentation algorithm efficiently and effectively detects non-ideal eye boundaries," said Richa Singh, Professor, Department of Computer Science and Engineering, IIT-Jodhpur.


Microsoft's Newest AI technology, "PeopleLens," is Helping Blind People See

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Microsoft debuted a slew of new AI technologies at their annual Ignite conference. One of the most interesting is an AI system called "PeopleLens." PeopleLens is a platform that uses computer vision algorithms to help blind people engage with their social surroundings. The system is designed to identify and interpret objects in the user's environment and relay those details back to the user in a way that they can understand. This opens a world of possibilities for blind people, who until now have been largely cut off from social interaction. With PeopleLens, they can now participate in conversations, navigate their surroundings, and generally experience the world in a way that was once impossible.


Microsoft's Newest AI technology, "PeopleLens," is Helping Blind People See

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The aim was to create a machine learning system to help blind people … They then used deep learning algorithms to train a computer vision model …


The tech savvy physician: How AI will transform your practice

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Artificial intelligence (AI) is no longer just the future of medicine--it is already here, and over time it will transform nearly every area of medical practice, according to experts. AI involves machine learning, where computers get smarter at seeking patterns or connections the more data is input; natural language processing, where computers learn to read and analyze unstructured clinical notes or patient reports; robotic process automation, such as chat bots; diagnostic capabilities such as IBM's Watson; and even more processes that help with patient adherence and administrative tasks. "AI is impacting health care at every level, from the provider to the payer to pharma," according to Dan Riskin, MD, CEO and founder of Verantos, a health care data company in Palo Alto, California, that uses AI to sort through real world evidence. "AI is utilized in a multitude of ways depending on the health care ecosystem," added Athena Robinson, PhD, chief clinical officer at Woebot Labs, a digital therapeutics company in San Francisco. "Some folks think of augmented systems, such as transactional bots that you call to schedule an appointment."


'We need to be much more diverse': More than half of data used in health care AI comes from the U.S. and China

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As medicine continues to test automated machine learning tools, many hope that low-cost support tools will help narrow care gaps in countries with constrained resources. But new research suggests it's those countries that are least represented in the data being used to design and test most clinical AI -- potentially making those gaps even wider. Researchers have shown that AI tools often fail to perform when used in real-world hospitals. It's the problem of transferability: An algorithm trained on one patient population with a particular set of characteristics won't necessarily work well on another. Those failures have motivated a growing call for clinical AI to be both trained and validated on diverse patient data, with representation across spectrums of sex, age, race, ethnicity, and more.


Deep learning algorithm shows accuracy in detecting glaucoma on fundus photographs

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Automated deep learning analysis of fundus photographs showed high diagnostic accuracy in determining primary open-angle glaucoma, with increased ability to detect glaucoma earlier than human readers. A deep learning (DL) algorithm was trained, validated and tested on the fundus stereophotographs of participants enrolled in the Ocular Hypertension Treatment Study (OHTS), a randomized clinical trial evaluating the safety and efficacy of IOP-lowering medications in preventing progression from ocular hypertension to primary open-angle glaucoma (POAG). Assessment of optic disc and visual field changes in the OHTS was performed by two reading centers and a masked committee of glaucoma specialists, "a demanding, laborious and complicated task," according to the authors. The OHTS data set consisted of fundus photographs from 1,636 participants, of which 1,147 were included in the training set, 167 in the validation set and 322 in the test set. The DL model detected conversion to POAG with high diagnostic accuracy, suggesting that artificial intelligence can offer a reliable tool to automate the determination of glaucoma for clinical trial management, simplifying the process of human interpretation and, possibly, making it more standardized, objective and accurate.


AI Detects Diabetic Retinopathy in Real-Time

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By 2050, the National Institute of Health (NIH) National Eye Institute estimates that 14.6 million Americans will have diabetic retinopathy. A new study published in The Lancet demonstrates how artificial intelligence (AI) machine learning can screen in real-time for diabetic retinopathy--a leading cause of preventable blindness, particularly in areas with low-income or middle-income economies. According to the Centers for Disease Control (CDC), one in four American adults with vision loss reported anxiety or depression. Moreover, vision loss has been linked to fear, anxiety, worry, social isolation, and loneliness. Scientists affiliated with Google Health and their collaborators applied artificial intelligence (AI) machine learning to detect one of the most common causes of preventable blindness--diabetic retinopathy.