Can Tele-Neuro-Ophthalmology Be Useful Beyond the Pandemic?

Abstract

Purpose of the review: Neuro-ophthalmologists rapidly adopted telehealth during the COVID-19 pandemic to minimize disruption to patient care. This article reviews recent research on tele-neuro-ophthalmology adoption, current limitations, and potential use beyond the pandemic. The review considers how digital transformation, including machine learning and augmented reality, may be applied to future iterations of tele-neuro-ophthalmology.

Recent findings: Telehealth utilization has been sustained among neuro-ophthalmologists throughout the pandemic. Adoption of tele-neuro-ophthalmology may provide solutions to subspecialty workforce shortage, patient access, physician wellness, and trainee educational needs within the field of neuro-ophthalmology. Digital transformation technologies have the potential to augment tele-neuro-ophthalmology care delivery by providing automated workflow solutions, home-based visual testing and therapies, and trainee education via simulators. Tele-neuro-ophthalmology use has and will continue beyond the COVID-19 pandemic. Digital transformation technologies, when applied to telehealth, will drive and revolutionize the next phase of tele-neuro-ophthalmology adoption and use in the years to come.

Keywords: Artificial intelligence; Augmented reality; Neuro-ophthalmology; Pandemic; Telehealth; Telemedicine.

Fig. 1

The interplay of synchronous and asynchronous telehealth with incorporation of artificial intelligence. Synchronous telehealth is live/interactive telehealth that can be either clinic or home based. It includes video or phone visits. Asynchronous telehealth is a “store-and-forward” approach where testing/information is obtained and then shared through a portal for a physician to review later. Hybrid telehealth incorporates both synchronous and asynchronous modalities including capturing a visual field or fundus photograph asynchronously for the neuro-ophthalmologist to review at a later time point. A video visit is then scheduled for the neuro-ophthalmologist to synchronously review with the patient the testing results and care management plan. An example of a potential artificial intelligence (AI)-driven future state of neuro-ophthalmology is where asynchronous data is obtained via augmented reality (AR) home monitoring of a visual field defect. Deep learning (DL) algorithms then interpret the change in the visual field defect from previously obtained visual fields of the patient. If there are significant changes, DL can then pivot to either alert scheduling of an urgent synchronous video visit or a routine video visit with the patient

Fig. 2

Artificial intelligence, machine learning, and deep learning defined. AI is the broad umbrella concept under which machine and deep learning reside. AI is defined as any technique that is used to mimic sentient behavior. Machine learning (ML) is a form of AI focused on data analysis by the construction and study of algorithms that process data to develop prediction or decision models. Deep learning (DL) is a form of machine learning that mimics human learning by layering multiple automated learning algorithms over the data in an iterative process to achieve the desired level of accuracy