Marian Blazes – Page 2 – Computational Ophthalmology

August 10, 2023August 10, 2023

Evaluating Access to Laser Eye Surgery by Driving Times Using Medicare Data and Geographical Mapping

In this innovative study published in JAMA Ophthalmology, Shaffer et al. used various data sources to investigate patients' access to laser eye surgery providers in several states which recently expanded laser surgery privileges to optometrists. Laser eye surgery has been traditionally limited to ophthalmologists, but optometrists have been advocating to perform these procedures, making the argument that patients in rural areas may have limited access to eye care.

To investigate whether access to care had improved in states that have already expanded laser surgery privileges to optometrists, the authors looked at Medicare part B claims data as well census, geographic and traffic data to determine percentages of the Medicare patients who live within 30 minutes driving time from an ophthalmologist or optometrist, and to estimate travel times for patients to their closest care provider.

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August 10, 2023August 10, 2023

Race, Ethnicity, Insurance and Population Density Associations with Pediatric Strabismus and Strabismic Amblyopia in the IRIS® Registry

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In this paper published in Ophthalmology, Rajesh et al. evaluated sociodemographic and clinical factors and their association with the management and outcomes of strabismus (misalignment of the eyes) in children. Treatment for strabismus can be very successful if detected early. Previous studies have found that due to delayed diagnosis and/or unsuccessful treatment, certain groups of children with strabismus may be at higher risk for amblyopia, or vision loss when the normal connection between the eye and brain does not develop normally.

The American Academy of Ophthalmology IRIS® Registry (Intelligent Research In Sight) database is a comprehensive collection of clinical eye data collected from more than 78 million patients and more than 15,000 unique healthcare providers across the United States. Due to its size and comprehensiveness, the IRIS Registry offers a unique opportunity to investigate strabismus outcomes. The authors looked at records from more than 160,000 patients in the IRIS Registry to determine their age at diagnosis, whether or not they had resulting amblyopia, and whether or not they underwent surgery to correct their strabismus.

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March 17, 2023

Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations

In this paper, recently published in the journal Ophthalmology, Yue Wu and his co-authors developed a unique method for training a model to identify the retinal layers on optical coherence tomography images, with the key feature that the model works successfully on optical coherence tomography (OCT) images from a different OCT device than the one that was used to obtain the training data. Although the field of deep learning image analysis is rapidly advancing, one major roadblock is the problem of domain shift, where models trained on a particular dataset may experience significant performance degradation when applied to slightly different datasets from different hospitals, imaging protocols, or device manufacturers.

September 17, 2022September 21, 2022

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.

August 25, 2022August 26, 2022

Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging

In a new paper published in the journal Ophthalmology, Lee Lab members Yuka Kihara and Aaron Lee report the development of a novel deep learning approach to address a key problem in glaucoma research and treatment - predicting visual function from structural changes detected on optical coherence tomography (OCT). Physicians rely on visual field testing to assess whether glaucoma patients will progress to more severe disease, but the test is notoriously challenging for patients and the results can be inconsistent. Advanced retinal imaging such as OCT can reveal key structural changes in the retina and optic nerve, but previous methods for linking structural changes to visual function outcomes have been challenging. This approach provides a fully automated method for improving the prediction of standard automated perimetry sensitivity directly from structural imaging data and also enables artificial intelligence–derived structure–function mapping.