machine learning – Computational Ophthalmology

Student becomes teacher: training faster deep learning lightweight networks for automated identification of optical coherence tomography B-scans of interest using a student-teacher framework

Marian Blazes — Thu, 01 Jul 2021 21:44:00 +0000

In this study, Dr. Julia Owen and her co-authors developed a novel method for automated detection of abnormal optical coherence tomography (OCT) B-scan ("B-scan of interest") images of the retina. Commercial OCT devices do not routinely provide automated diagnoses, so disease detection requires expert interpretation of the images by an ophthalmologist. Reliable automated detection of …

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Protecting Data Privacy in the Age of AI-Enabled Ophthalmology

Marian Blazes — Sat, 11 Jul 2020 01:31:49 +0000

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.

Clinical Applications of Continual Learning Machine Learning

Marian Blazes — Mon, 01 Jun 2020 18:03:53 +0000

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.

How Artificial Intelligence Can Transform Randomized Controlled Trials

Marian Blazes — Sat, 15 Feb 2020 20:32:00 +0000

In this commentary for the journal Translation Vision Science and Technology, Dr. Aaron Lee and Dr. Cecilia Lee discuss how the application of artificial intelligence and big data in healthcare has the potential to transform clinical trial research. They explain some of the common pitfalls of randomized controlled trials and how deep learning is particularly well-suited to overcome some of these problems, leading to more efficient execution and greater statistical power than what would be expected from traditional trials.

Logistic Regression Classification of Primary Vitreoretinal Lymphoma Versus Uveitis by Interleukin 6 and Interleukin 10 Levels

Marian Blazes — Wed, 05 Feb 2020 22:09:00 +0000

In this study published in the journal Ophthalmology, the authors evaluated the use of a logistic regression model for early diagnosis of primary vitreoretinal lymphoma. Primary vitreoretinal lymphoma is a rare disease with a generally poor prognosis. Early diagnosis of local ocular disease has been shown to prolong survival significantly, but because the disease is rare …

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Development and validation of a machine learning, smartphone-based tonometer

Marian Blazes — Mon, 23 Dec 2019 18:15:00 +0000

Access to eye care in more rural and remote areas can be challenging, because ophthalmologists need certain types of examination equipment that can be large and difficult to transport. Advances in smartphone technology are helping to address this problem - better cameras, software, and hardware attachments have the potential to allow ophthalmologists to bring a …

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To Err is Both Human and AI

Marian Blazes — Wed, 10 Apr 2019 18:19:00 +0000

In a recent Outlook commentary in the journal Nature, Dr. Aaron Lee discusses the use of artificial intelligence (AI) in clinical medicine - both the significant potential for this technology as well as the various limitations and ethical issues involved with its implementation. He specifically addresses the case of AI screening systems for diabetic retinopathy …

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Fully automated, deep learning segmentation of oxygen-induced retinopathy images

Marian Blazes — Thu, 21 Dec 2017 23:14:00 +0000

In this paper, published in JCI Insight, the authors describe their deep learning model for quantifying vaso-obliteration and neovascularization on retinal images, which are key measurements in oxygen-induced retinopathy mouse model studies of ischemia-driven neovascularization. The mouse model of oxygen-induced retinopathy is one of the most commonly used in vivo models to study basic mechanisms …

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