Comparison of a New Head Mount Virtual Reality Perimeter (C3 Field Analyzer) With Automated Field Analyzer in Neuro-Ophthalmic Disorders.

BACKGROUND: Automated perimetry in neurologically disabled patients is a challenge. We have devised a patient-friendly virtual reality perimeter, the C3 field analyzer (CFA). We aim to assess the utility of this as a visual field-testing device in neuro-ophthalmic patients for screening and monitoring. METHODS: Neuro-ophthalmic patients and controls were selected to participate in the study between September and December 2018. They randomly underwent either the CFA or automated field analyzer (HFA) first followed by the other in an undilated state. The CFA results were compared with the HFA, and the correlation of the pattern of the field defect was assessed by an independent masked physician. RESULTS: In total, 59 eyes of 33 neuro-ophthalmic patients (cases) and another 95 normal individuals (controls) were enrolled. CFA was found to have greater proportion of reliable fields (81.4%) than HFA (59.3%) ( P = 0.009). There were less false negatives ( P < 0.001) and more false positives in CFA ( P < 0.001) among neuro-ophthalmic patients compared with controls. Among neuro-ophthalmology patients, the number of fixation losses was greater with CFA ( P < 0.001), whereas false negatives were greater in HFA ( P < 0.001). On assessing the pattern of the field defects, we found that there was almost 70% correlation of CFA with HFA. Moreover, in classical neurological fields such as hemianopia, the correlation was 87.5%. CONCLUSIONS: The CFA seems to correlate well with HFA in classic neurological fields such as hemianopias and may serve as an alternative in patients unable to perform a standard automated perimetry. Further developments are currently underway to incorporate threshold testing.

Automated diagnosis of diabetic retinopathy and glaucoma using fundus and OCT images.

We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.