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Diagnostic accuracy of an iPad application for detection of visual field defects.
BACKGROUND/OBJECTIVES: Tablet-based perimetry could be used to test for glaucomatous visual field defects in settings without easy access to perimeters, although few studies have assessed diagnostic accuracy of tablet-based tests. The goal of this study was to determine the diagnostic accuracy of iPad perimetry using the visualFields Easy application. SUBJECTS/METHODS: This was a prospective, cross-sectional study of patients undergoing their first Humphrey Field Analyser (HFA) visual field test at a glaucoma clinic in India. Participants underwent 24-2 SITA Standard HFA testing and iPad-based perimetry with the visualFields Easy application. Reference standards for both visual field loss and suspected glaucoma were determined by ophthalmologist review of HFA results and optic disc photographs. Receiver operating characteristic curves were constructed to assess diagnostic accuracy at various test thresholds. RESULTS: 203 eyes from 115 participants were included, with 82 eyes classified as moderate or worse glaucoma. iPad perimetry had an area under the receiver operating characteristic (AUROC) curve of 0.64 (95% CI 0.57 to 0.71) for detection of any visual field defect relative to HFA and an AUROC of 0.68 (0.59 to 0.76) for detection of moderate or worse glaucoma relative to ophthalmologist examination. At a set specificity of 90%, the sensitivity of iPad perimetry for detection of moderate or worse glaucoma was 35% (22-48%). CONCLUSIONS: iPad perimetry using the visualFields Easy application had inadequate diagnostic accuracy to be used as a screening tool for glaucoma in this South Indian population
A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis
Objective: Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices.
Research design and methods: We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia.
Results: In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920-0.954), 0.958 (0.930-0.977), and 0.965 (0.948-0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940-0.995), 0.951 (0.898-0.982), and 0.975 (0.947-0.991) for the primary data set and >0.894 for the external data sets.
Conclusions: We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics