RESUMEN
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.
Asunto(s)
Técnicas de Diagnóstico Oftalmológico , Interpretación de Imagen Asistida por Computador/métodos , Retina , Programas Informáticos , Trastornos de la Visión , Adolescente , Adulto , Coriorretinopatía Serosa Central/complicaciones , Coriorretinopatía Serosa Central/diagnóstico , Coriorretinopatía Serosa Central/fisiopatología , Femenino , Humanos , Degeneración Macular/complicaciones , Degeneración Macular/diagnóstico , Degeneración Macular/fisiopatología , Masculino , Retina/diagnóstico por imagen , Retina/fisiopatología , Tomografía de Coherencia Óptica , Trastornos de la Visión/diagnóstico , Trastornos de la Visión/etiología , Trastornos de la Visión/fisiopatología , Adulto JovenRESUMEN
This paper presents a new mathematical model along with a measurement platform for accurate detection and monitoring of various visual distortions (VD) caused by macular disorders such as central serous chorioretinopathy (CSR) and age-related macular degeneration (AMD). This platform projects a series of graphical patterns on the patient's retina and calculates the severity of VDs accordingly. The accuracy of this technique relies on the accurate detection of distorted lines by the patient. We also propose a simple mathematical model to evaluate the VD created by CSR. The model is used as a control for the test results achieved from the proposed platform. The proposed platform consists of the required hardware and software for the generation and projection of patterns along with the collection and processing of patients against their standard optical coherence tomography (OCT) images. Based on these results, the OCT images agree with the VD test results, and the proposed platform can be used as an alternative home monitoring method for various macular disorders.
RESUMEN
Recent advances of computerized graphical methods have received significant attention for detection and home monitoring of various visual distortions caused by macular disorders such as macular edema, central serous chorioretinopathy, and age-related macular degeneration. After a brief review of macular disorders and their conventional diagnostic methods, this paper reviews such graphical interface methods including computerized Amsler Grid, Preferential Hyperacuity Perimeter, and Three-dimensional Computer-automated Threshold Amsler Grid. Thereafter, the challenges of these computerized methods for accurate and rapid detection of macular disorders are discussed. The early detection and progress assessment of macular disorders can significantly enhance the required clinical procedure for the diagnosis and treatment of macular disorders.