Artificial Intelligence-Based Predictions in Neovascular Age-Related Macular Degeneration

Daniela Ferrara; Elizabeth M. Newton; Aaron Y. Lee

Disclosures

Curr Opin Ophthalmol. 2021;32(5):389-396. 

In This Article

Applications of Artificial Intelligence-based Neovascular Age-related Macular Degeneration Treatment Predictions

Artificial intelligence-based nAMD treatment predictions have potential applications for both clinical research and clinical practice, with the goal of achieving the best visual outcome for each individual patient.

In clinical research, artificial intelligence-based models could improve clinical trial design, including patient identification, selection, and randomization, as well as adjustments in trial analysis. Artificial intelligence can also improve efficiency and standardization of image grading, enabling analysis on a larger and more detailed scale than possible with current practices and standard technologies. For smaller, early-stage studies, or those with heterogeneous populations, application of artificial intelligence-based models could improve understanding of treatment responses and increase confidence in decision making. Artificial intelligence can also create 'synthetic' treatment arms, that is, hypothetical, simulated comparator arms that could be used to model additional patient populations or alternative treatments for clinical trials, including sham arms.[44,45]

In clinical practice, considering treatment options for nAMD currently available, physician decisions are limited by the optimal treatment regimens for maximum visual gains and least treatment burden. As an extreme example, physicians following a monthly treatment regimen would not have a compelling motivation to use an artificial intelligence-based prediction model. However, in the near future, complexity of treatment decisions is expected to increase with the expansion of the nAMD treatment landscape to potentially include new mechanisms of action, long-acting delivery options, and gene therapy.[2]

Within the field of retina, and particularly OCT image analysis, artificial intelligence has the potential to assist physicians in elucidating individual needs as quickly and accurately as possible, thereby improving patient care, in several ways. First, artificial intelligence can equip physicians with better models for efficient image analysis, which could expand the information readily available for making treatment decisions. Also, given the variety of clinical expertise, artificial intelligence could raise the bar of standard of care by providing insights into pathology that may fall outside a particular physician's day-to-day experience. Finally, artificial intelligence can extract features beyond what an expert can discern on individual images; for example, a deep learning model was developed to create OCT angiography-like images from structural OCT.[46]

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