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Protecting Data Privacy in the Age of AI-Enabled Ophthalmology

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In this commentary in the journal Translational Vision Science and Technology, Dr. Elysse Tom and her co-authors discuss digital data privacy in the era of "Big Data" and artificial intelligence. Although these new technologies offer many potential benefits for patients and for healthcare systems, there is a need for better data protection methods that can evolve with these advances.

The authors discuss some of the ethical issues associated with the use of these increasingly large clinical datasets, noting that maintaining data privacy and confidentiality-and thus respect for persons-is a challenge. For example, breaches of patient privacy can cause major harms and can also have unintended consequences, such as impacting employment or insurance coverage or allowing hackers to obtain Social Security numbers and personal financial information.

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Evolving consensus for immunomodulatory therapy in non-infectious uveitis during the COVID-19 pandemic

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In this report of results from a survey of international uveitis experts, the authors provide guidance on the use of immunomodulatory therapy for systemic treatment of non-infectious uveitis during  the coronavirus disease-2019 (COVID-19) pandemic.

Noninfectious uveitis is usually treated with corticosteroids and conventional immunosuppressive agents. Biologics are used when long-term treatment is required and a corticosteroid-sparing approach is necessary. One of the most important concerns related to immunomodulatory therapy is the increased risk of infections, as these drugs act by limiting the patient's immune responses. Patients who may be at additional risk of infection with coronavirus and/or a more severe course of, or even fatality from, COVID-19. Therefore, during the pandemic, there is an urgent need for guidance on how to manage patients with uveitis.

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Predictors of Narrow Angle Detection Rate-A Longitudinal Study of Massachusetts Residents Over 1.7 Million Person Years

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In this large retrospective study published in the journal Eye, Dr. Cecilia Lee and her co-authors reviewed data from a Massachusetts claims database to assess how certain risk factors for glaucoma are detected and diagnosed during an eye exam. The authors compared outcomes from both ophthalmologists and optometrists to determine if there were any differences in how often they referred at-risk patients for further evaluation and treatment.

Primary angle closure glaucoma is one of the leading causes of blindness worldwide, affecting ~26% of the glaucoma population globally. Timely detection of occludable narrow angles (condition that can sometimes progress to primary angle closure glaucoma) in patients who are at risk is a key method of prevention. If indicated, patients can be referred for laser peripheral iridotomy to prevent more serious outcomes.

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Clinical Applications of Continual Learning Machine Learning

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In a recent commentary in The Lancet Digital Health, Dr. Cecilia Lee and Dr. Aaron Lee address artificial intelligence and its role in the practice of medicine, specifically potential uses for continual learning models. Continual learning is a type of machine learning in which the model continues to learn and improve as it receives new data while retaining what it has previously learned.

This ability to continuously fine-tune their performance makes continual learning models better suited for certain complex tasks than the more traditional machine learning devices used in clinical practice today. But the fact that they constantly update and change also makes these models more complicated to implement into patient care systems. The authors discuss the practical, ethical, and regulatory issues that must be addressed before these models can be fully integrated into clinical practice.

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Using Deep Learning to Automate Goldmann Applanation Tonometry Readings

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This study describes a novel method for automating Goldman Applanation Tonometry (GAT) measurements using a deep learning approach. GAT is the gold standard method for measuring intraocular pressure, which is an essential metric in the management of glaucoma.

To obtain accurate intraocular pressure measurements using traditional GAT, the user must use a dial to adjust the device to align two visible circular "mires", in order to determine the correct amount of force needed to create a fixed area of applanation on the surface of the eye, which is then used to calculate the intraocular pressure. This somewhat subjective alignment procedure can produce different results between users, and studies have found other unintended biases such as a preference for even-numbered readings on the dial. The goal of this study was to provide a method for obtaining more objective and reproducible GAT readings, by training a deep learning based algorithm to accurately recognize and measure the mires produced from a fixed application of force, and then use the measurements to calculate the intraocular pressure.

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