Decision Support System for Detection of Papilledema through Fundus Retinal Images.

A condition in which the optic nerve inside the eye is swelled due to increased intracranial pressure is known as papilledema. The abnormalities due to papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated optic disc capillaries, blurred disc margins, absence of venous pulsations, elevation of optic disc, obscuration of optic disc vessels, dilation of optic disc veins, optic disc splinter hemorrhages, cotton wool spots and hard exudates may result in complete vision loss. The ophthalmologists detect papilledema by means of an ophthalmoscope, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound. Rapid development of computer aided diagnostic systems has revolutionized the world. There is a need to develop such type of system that automatically detects the papilledema. In this paper, an automated system is presented that detects and grades the papilledema through analysis of fundus retinal images. The proposed system extracts 23 features from which six textural features are extracted from Gray-Level Co-occurrence Matrix (GLCM), eight features from optic disc margin obscuration, three color based features and seven vascular features are extracted. A feature vector consisting of these features is used for classification of normal and papilledema images using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. The variations in retinal blood vessels, color properties, texture deviation of optic disc and its peripapillary region, and fluctuation of obscured disc margin are effectively identified and used by the proposed system for the detection and grading of papilledema. A dataset of 160 fundus retinal images is used which is taken from publicly available STARE database and local dataset collected from Armed Forces Institute of Ophthalmology (AFIO) Pakistan. The proposed system shows an average accuracy of 92.86% for classification of papilledema and normal images. It also shows an average accuracy of 97.85% for classification of already classified papilledema images into mild and severe papilledema. The proposed system is a novel step towards automated detection and grading of papilledema. The results showed that the technique is reliable and can be used as clinical decision support system.

Clinical and Radiological Characteristics of Non-Obese Female Patients with Idiopathic Intracranial Hypertension.

While the typical patient with idiopathic intracranial hypertension (IIH) is an obese female of childbearing age, there are unique patient populations, such as non-obese females, that have not been well studied. Characterizing this subpopulation may increase awareness our of it, which may prevent underdiagnosis and improve our understanding of IIH's underlying pathophysiology. We retrospectively reviewed electronic medical records and compared the clinical and radiological characteristics of non-obese (BMI < 30) and obese (BMI > 30) female patients with IIH. Two hundred and forty-six patients (age 32.3 ± 10) met our inclusion criteria. The non-obese patients (n = 59, 24%) were significantly younger than the obese patients (29.4 ± 9.9 vs. 33.2 ± 10.2, p = 0.004) and had higher rates of severe papilledema (Friesen 4-5; 25.4% vs. 11.8%, p = 0.019), scleral flattening (62.7% vs. 36.9%, p = 0.008), and optic nerve dural ectasia (78.0% vs. 55.6%, p = 0.044). Non-obese patients also had a tendency to have a higher lumbar puncture opening pressure (368 ± 92.7 vs. 344 ± 76.4, p = 0.062). Non-obese patients were three times more likely to present with a combination of scleral flattening and optic nerve dural ectasia (OR = 3.00, CI: 1.57-5.72, χ2 = 11.63, α < 0.001). Overall, non-obese females with IIH were found to have a more fulminant presentation, typified by higher rates of severe papilledema and radiological findings typical for IIH.

Automatic detection of papilledema through fundus retinal images using deep learning.

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.