Optical And Nonoptical Aids For Reading And Writing In Individuals With Acquired Low Vision.

To evaluate the use of optical and nonoptical aids during reading and writing activities in individuals with acquired low vision. This study was performed using descriptive and cross-sectional surveys. The data collection instrument was created with structured questions that were developed from an exploratory study and a previous test based on interviews, and it evaluated the following variables: personal characteristics, use of optical and nonoptical aids, and activities that required the use of optical and nonoptical aids. The study population included 30 subjects with acquired low vision and visual acuities of 20/200-20/400. Most subjects reported the use of some optical aids (60.0%). Of these 60.0%, the majority (83.3%) cited spectacles as the most widely used optical aid. The majority (63.3%) of subjects also reported the use of nonoptical aids, the most frequent ones being letter magnification (68.4%), followed by bringing the objects closer to the eyes (57.8%). Subjects often used more than one nonoptical aid. The majority of participants reported the use of optical and nonoptical aids during reading activities, highlighting the use of spectacles, magnifying glasses, and letter magnification; however, even after the use of these aids, we found that the subjects often needed to read the text more than once to understand it. During writing activities, all subjects reported the use of optical aids, while most stated that they did not use nonoptical aids for such activities.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.