NGRID: A novel platform for detection and progress assessment of visual distortion caused by macular disorders

This paper presents a new graphical macular interface system (GMIS) for accurate, rapid, and quantitative measurement of visual distortion (VD) in the central vision of patients suffering from macular disorders. In this system, a series of predefined graphical patterns or multiple grids (NGRID) are randomly selected from a library of patterns and visualized on the screen, then the VDs identified by the patient are recorded as binary codes using various control methods including speech recognition. Scalable Vector Graphics (SVG) is used to generate the patterns and save them into a central library. Based on the projected patterns and the patients' responses, a VD graph or so-called heatmap is generated for eye-care purposes. We demonstrate and discuss the functionality of the proposed system for the detection and progress assessment of a macular condition in patients suffering from Central Serous Chorioretinopathy (CSR). Also, we characterize the proposed technique to evaluate the systematic error and response time on healthy human subjects with normal vision. Based on these results, the voice recognition input method exhibits a lower error but a higher response time compared to other input devices. We run the proposed NGRID VD technique to evaluate the effect of CSR on the visual field of a CSR patient. The generated heatmaps are in agreement with standard Optical Coherence Tomography (OCT) images obtained at different times from both the left and right eyes. These results reveal the applicability of the proposed technique for the detection and assessment of macular disorders. Based on these results, the proposed NGRID platform shows great promise for use as an alternative solution for in-home monitoring of various macular disorders and as a means of forwarding responses to secured cloud facilities for future data analysis.