AMD is characterized by 4 distinct stages depending on physiological characteristics. Intermediate AMD (iAMD) is a critical stage for many individuals as it can be characterized by significant retinal changes, initial visual deficits, and is a risk factor for progression to the advanced forms: neovascular (“wet”) and geographic atrophy (“dry”).  

Current clinical practice has established best-corrected visual acuity (BCVA) as the primary functional endpoint for AMD. However, recent research has noted that visual acuity may not be significantly altered by AMD progression, but contrast sensitivity may be a better metric for assessing functional deficits. (Vingopoulos et al 2021, Mones and Rubin) As such, a new functional tool called the quantitative contrast sensitivity function (qCSF) test has gained interest in monitoring AMD progression as it is able to differentiate between AMD staging based on its clinical results. With the help of AI, the test uses a Bayesian machine-learning model to adapt letter sizing (spatial frequency) and letter contrast depending on the patient’s correctness. Once the test is completed, qCSF provides critical metrics, such as contrast acuity (CA), that provide insight into the individual’s visual deficits, longitudinal progression, and retinal changes.  

Specifically, our study reveals that certain biomarkers of iAMD, including subretinal drusenoid deposits and hyperreflective foci, are associated with changes in qCSF performance. Moreover, our results identified risk factors for AMD progression in both retinal biomarkers and qCSF metrics, further demonstrating that the test may prove useful in determining if progression is likely. This study helps to establish the importance of the qCSF test and its AI tools in monitoring AMD progression and its impact on visual function.