Computational Ophthalmology – Page 7 – big data. machine learning. data science.

March 31, 2020October 15, 2020

Smoking Is Associated with Higher Intraocular Pressure Regardless of Glaucoma.

Cigarette smoking is known to play a role in several ophthalmologic diseases, but reports about the effect of smoking on intraocular pressure and glaucoma have had conflicting results. In this study published in the journal Ophthalmology Glaucoma, Drs. Cecilia and Aaron Lee and their co-authors takes advantage of the American Academy of Ophthalmology IRIS (Intelligent Research in Sight) Registry, a database of 60+ million unique patients and 15,000 eye care providers to examine the connection between smoking history and intraocular pressure in more than 12 million smokers, past smokers, and nonsmokers. The authors controlled for other factors known to raise IOP (age, sex, cataract surgery, glaucoma surgery) and compared mean interocular pressure between patients with and without glaucoma according to their smoking history. They found that intraocular pressure was highest in the smoking group, a trend that was present in almost every year of life.

March 23, 2020October 15, 2020

Prognostic utility of whole genome sequencing and polymerase chain reaction tests of ocular fluids in post-procedural endophthalmitis

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Endophthalmitis is a rare but significant potential complication of eye surgery that can result in a substantial loss of vision. Microbial culture remains the gold standard for detecting organisms associated with endophthalmitis, but in 30% of cases no organism is identified. Molecular testing such as PCR or deep DNA sequencing have been increasingly used in ophthalmology, and can identify and characterize pathogens with unprecedented detail. We sought to apply these techniques to a prospective cohort of patients with endophthalmitis and correlate the findings with clinical outcomes, to determine whether they provide additional prognostic information for the management of post-procedure endophthalmitis.

February 16, 2020October 15, 2020

Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review

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In this review article for Translational Vision Science and Technology, Dr. Ryan Yanagihara and his co-authors discuss the progress and future directions of deep learning applications for diagnosing retinal disease from optical coherence tomography imaging.

Deep learning is a subfield of machine learning that involves convolutional neural networks. Within ophthalmology, deep learning has been applied to automated diagnosis, segmentation, big data analysis, and outcome predictions. Many recent studies have successfully used deep learning for OCT image analysis, in order to diagnose and segment features of diabetic retinopathy, age-related macular degeneration, and glaucoma.

February 15, 2020October 15, 2020

How Artificial Intelligence Can Transform Randomized Controlled Trials

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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.

February 13, 2020September 28, 2020

Data-Driven, Feature-Agnostic Deep Learning vs Retinal Nerve Fiber Layer Thickness for the Diagnosis of Glaucoma

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In this commentary in the journal JAMA Ophthalmology, Christine A. Petersen, MD, Parmita Mehta, MS, and Aaron Y. Lee, MD, MSCI review a recent article published in the same journal, "Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans."

The commentary addresses why a deep learning approach was more successful at detecting glaucoma using spectral domain optical coherence tomography scans than the more traditional strategy of performing automated segmentation to analyze retinal nerve fiber layer parameters.