Lee Lab launches AI-READI, a unique data generation project

AI-READI is part of the Bridge to Artificial Intelligence (Bridge2AI) program, a new initiative by the National Institutes of Health to expand the use of artificial intelligence in biomedical and behavioral research

The National Institutes of Health (NIH) Common Fund has launched the Bridge2AI program to accelerate the widespread use of artificial intelligence (AI) by the biomedical and behavioral research communities. The Bridge2AI program will be assembling team members from diverse disciplines and backgrounds to generate tools, resources, and richly detailed data that are responsive to AI approaches. Through extensive collaboration across projects, Bridge2AI researchers will create guidance and standards for the development of ethically sourced, state-of-the-art, AI-ready data sets that have the potential to help solve some of the most pressing challenges in human health. 

NIH has issued four awards for data generation projects to generate new biomedical and behavioral data sets ready to be used for developing AI technologies, and three awards to create a Bridge Center to integrate activities and knowledge across data generation projects while disseminating products, best-practices, and training materials. 

Cecilia and Aaron Lee will be the co-principal investigators of one of the the data generation projects, AI Ready and Equitable Atlas for Diabetes Insights (AI-READI). The goal of AI-READI is to develop a multi-modal atlas of type 2 diabetes mellitus (T2DM) by collecting data from a diverse population, while simultaneously creating a roadmap for ethical and equitable research that focuses on the diversity of the research participants as well as the workforce involved at all stages of the research process (collection, curation, and analysis). The AI-READI dataset will collect a range of health data from more than 4000 participants with diverse racial/ethnic backgrounds who represent all stages of T2DM, allowing researchers to better investigate health outcomes associated with T2DM in previously understudied populations, many of which have been significantly impacted by this disease. The project is structured with cross-disciplinary modules that focus on different aspects of the project, including data collection, team building, ethical oversight, development of a skilled diverse workforce, and the creation of data collection tools and standards. 

Read the UW Newsroom article about this exciting initiative here: UW Medicine will lead study arm of national AI initiative

To learn more about the Bridge2AI program, visit the Musings from the Mezzanine blog from the National Library of Medicine, watch this video about the Bridge2AI program, and read the NIH press release.

Endophthalmitis Rate in Immediately Sequential versus Delayed Sequential Bilateral Cataract Surgery within the Intelligent Research in Sight (IRIS) Registry Data

This large retrospective cohort study used data from more than 5 million people who underwent bilateral cataract surgery in the United States to investigate the rate of post-operative endophthalmitis. The authors compared the rates of endophthalmitis in the group of patients who had both eye surgeries on the same day (immediate sequential bilateral cataract surgery, or ISBCS) to the group of patients who either had cataract surgery in both eyes on separate days (delayed sequential bilateral cataract surgery) or who just had surgery in one eye. The authors found that after controlling for age, sex, race, insurance status, and comorbid eye diseases, the risk of postoperative endophthalmitis was not statistically significantly different between these two groups, . 

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Big data requirements for artificial intelligence

In this article for the journal Current Opinion in Ophthalmology, Dr. Wang and her co-authors discuss the evolution of big data and artificial intelligence technologies in medicine, and describe some of the problems that must be addressed for big data to successfully enable the next generation of artificial intelligence.

Big data research has benefitted from important technological advances in recent years. Artificial intelligence research depends on large amounts of data, often collected from multiple institutions, in order to be most effective. More powerful computing resources, implementation of electronic health records, improved data collection, and efforts to create standards for data exchange have all helped researchers aggregate larger datasets. But there are still some important limitations to accessing big data for this kind of research, which the authors discuss in detail.

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Protecting Data Privacy in the Age of AI-Enabled Ophthalmology

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

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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