Evaluating the Performance of qVFM in Mapping the Visual Field of Simulated Observers With Eye Diseases.

Purpose: Recently, we developed a novel active learning framework, qVFM, to map visual functions in the visual field. The method has been implemented and validated in measuring light sensitivity and contrast sensitivity VFMs of normal observers. In this study, we evaluated the performance of the qVFM method in mapping the light sensitivity VFM of simulated observers with peripheral scotoma, glaucoma, age-related macular degeneration, and cataract. Methods: For each simulated patient, we sampled 100 locations (60 x 60 degrees) of the visual field and compared the performance of the qVFM method with a procedure that tested each location independently (the qYN method) in a cued Yes/No task. Two different switching modules, the distribution sampling method (DSM) and parameter delivering method (PDM), were implemented in the qVFM method. Simulated runs of 1200 trials were used to compare the accuracy and precision of the qVFM-DSM, qVFM-PDM and qYN methods. Results: The qVFM method with both switching modules can provide an accurate, precise, and efficient assessment of light sensitivity VFM for the simulated patients, with the qVFM-PDM method better at detecting VFM deficits in glaucoma. Conclusions: The qVFM method can be used to characterize residual vision of simulated ophthalmic patients. The study sets the stage for further investigation with real patients. Translational Relevance: The study shows that the qVFM method, with additional tests on real patients, can be potentially translated into clinical practice.