AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices.

Background: Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices.

Toward safer ophthalmic artificial intelligence via distributed validation on real-world data.

Purpose Of Review: The current article provides an overview of the present approaches to algorithm validation, which are variable and largely self-determined, as well as solutions to address inadequacies.

Recent Findings: In the last decade alone, numerous machine learning applications have been proposed for ophthalmic diagnosis or disease monitoring. Remarkably, of these, less than 15 have received regulatory approval for implementation into clinical practice.

Three-dimensional telesurgery and remote proctoring over a 5G network.

Purpose: To present 2 cases of vitreoretinal surgery performed on a three-dimensional (3D) heads-up display surgical platform with real-time transfer of 3D video over a fifth-generation (5G) cellular network.

Methods: An epiretinal membrane peel and tractional retinal detachment repair performed at Massachusetts Eye and Ear in April 2019 were broadcast live to the Verizon 5G Lab in Cambridge, MA.

Results: Both surgeries were successful.

Application of Deep Learning for Diagnosing, Classifying, and Treating Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) affects nearly 200 million people and is the third leading cause of irreversible vision loss worldwide. Deep learning, a branch of artificial intelligence that can learn image recognition based on pre-existing datasets, creates an opportunity for more accurate and efficient diagnosis, classification, and treatment of AMD on both individual and population levels. Current algorithms based on fundus photography and optical coherence tomography imaging have already achieved diagnostic accuracy levels comparable to human graders.

Accelerating ophthalmic artificial intelligence research: the role of an open access data repository.

Purpose Of Review: Artificial intelligence has already provided multiple clinically relevant applications in ophthalmology. Yet, the explosion of nonstandardized reporting of high-performing algorithms are rendered useless without robust and streamlined implementation guidelines. The development of protocols and checklists will accelerate the translation of research publications to impact on patient care.

Elective versus emergency abdominal surgery following cardiac transplantation: a Victorian state transplant service experience.

Background: Heart transplant patients constitute a unique patient cohort with multiple risk factors predictive of poor surgical outcome. The Alfred Hospital offers the only heart transplant service in Victoria, Australia. This article presents The Alfred Hospital's experience with outcomes of abdominal operations in the heart transplant patient population.

Extracorporeal membrane oxygenation in primary graft failure after heart transplantation.

Background: The aim of this review was to analyze our results with extracorporeal membrane oxygenation (ECMO) support for primary graft failure (PGF) in heart transplant recipients.

Methods: A retrospective review of 239 consecutive patients who underwent heart transplantation between January 2000 and August 2009 was performed. Orthotopic, heterotopic, and heart lung transplants were included in this analysis.