Idiopathic intracranial hypertension: Proposal of a stratification strategy for monitoring risk of disease progression

Onyia, Chiazor U.; Ogunbameru, Ibironke O.; Dada, Oluwamuyiwa A.; Owagbemi, Oluwafemi F.; Ige-Orhionkpaibima, Fred S.; Olarewaju, Oluseun A.; Komolafe, E O

doi:10.1016/j.clineuro.2019.02.013

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…35,36 Because the degree of papilledema at presentation is a reliable indicator of subsequent visual outcomes from secondary optic atrophy, [3][4][5][6][7][8][9] the severity of papilledema influences the management and the frequency of visual monitoring during follow-up of patients with elevated ICP. 37,38 Our simple binary classification of papilledema severity aimed at identification of patients with lower risk (mild to moderate papilledema) or higher risk (severe papilledema) of visual loss. 5,6,8,9 Hence, a patient with mild to moderate papilledema from IIH might be managed with weight loss and oral medications as an outpatient, whereas a patient with severe papilledema should have closer follow-up, sometimes inpatient, and benefit from timely surgical intervention, especially in those cases with visual loss.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

Vasseneix¹,

Najjar²,

Xu³

et al. 2021

Neurology

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Objective:To evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure, on standard retinal fundus photographs.Methods:A DLS was trained to automatically classify papilledema severity in 965 patients (2103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1052 photographs with mild/moderate papilledema (MP) and 1051 photographs with severe papilledema (SP) classified by a panel of experts. The performance of the DLS and that of three independent neuro-ophthalmologists were tested in 111 patients (214 photographs, 92 with MP and 122 with SP), by calculating the area under the receiver operating characteristics curve (AUC), accuracy, sensitivity and specificity. Kappa agreement scores between the DLS and each of the three graders and among the three graders were calculated.Results:The DLS successfully discriminated between photographs of MP and SP, with an AUC of 0.93 (95%CI: 0.89-0.96) and an accuracy, sensitivity and specificity of 87.9%, 91.8% and 86.2%, respectively. This performance was comparable with that of the three neuro-ophthalmologists (84.1%, 91.8% and 73.9%, P=0.19, P=1, P=0.09, respectively). Misclassification by the DLS was mainly observed for moderate papilledema (Frisén grade 3). Agreement scores between the DLS and the neuro-ophthalmologists’ evaluation was 0.62 (CI 95% 0.57-0.68), whereas the inter-grader agreement among the three neuro-ophthalmologists was 0.54 (CI 95% 0.47-0.62).Conclusions:Our DLS accurately classified the severity of papilledema on an independent set of mydriatic fundus photographs, achieving a comparable performance with that of independent neuro-ophthalmologists.Classification of Evidence:This study provides Class II evidence that a deep learning system using mydriatic retinal fundus photographs accurately classified the severity of papilledema associated in patients with a diagnosis of increased intracranial pressure.

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Section: Discussionmentioning

confidence: 99%

Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

Vasseneix¹,

Najjar²,

Xu³

et al. 2021

Neurology

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“…Several methods have been used to prevent, reduce, and treat various life-threatening diseases such as diabetes, cancer, atherosclerosis, cardiomyopathy, stroke, and heart attack [ 85 , 86 ]. Early diagnosis, coupled with efficient and effective monitoring of glucose levels in the body, is vital to keep diabetes under control [ 87 , 88 ].…”

Section: Multifunctional Applications Of Graphitic Carbon Nitride mentioning

confidence: 99%

Graphitic Carbon Nitride: A Highly Electroactive Nanomaterial for Environmental and Clinical Sensing

Idris

Oseghe

Msagati

et al. 2020

Sensors

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Graphitic carbon nitride (g-C3N4) is a two-dimensional conjugated polymer that has attracted the interest of researchers and industrial communities owing to its outstanding analytical merits such as low-cost synthesis, high stability, unique electronic properties, catalytic ability, high quantum yield, nontoxicity, metal-free, low bandgap energy, and electron-rich properties. Notably, graphitic carbon nitride (g-C3N4) is the most stable allotrope of carbon nitrides. It has been explored in various analytical fields due to its excellent biocompatibility properties, including ease of surface functionalization and hydrogen-bonding. Graphitic carbon nitride (g-C3N4) acts as a nanomediator and serves as an immobilization layer to detect various biomolecules. Numerous reports have been presented in the literature on applying graphitic carbon nitride (g-C3N4) for the construction of electrochemical sensors and biosensors. Different electrochemical techniques such as cyclic voltammetry, electrochemiluminescence, electrochemical impedance spectroscopy, square wave anodic stripping voltammetry, and amperometry techniques have been extensively used for the detection of biologic molecules and heavy metals, with high sensitivity and good selectivity. For this reason, the leading drive of this review is to stress the importance of employing graphitic carbon nitride (g-C3N4) for the fabrication of electrochemical sensors and biosensors.

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“…Several national and international guidelines regarding the investigations and management of IIH have been published in recent years [ 1 , 2 ] but there is still no consensus about the optimal organization of IIH-care [ 3 ]. There is also a paucity of studies in the literature on methods of monitoring disease progression in IIH [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Awareness, Diagnosis and Management of Idiopathic Intracranial Hypertension

Jensen

Cvetković

Korsbæk

et al. 2021

Life

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The diagnosis and management of idiopathic intracranial hypertension (IIH) can be difficult and multiple medical subspecialities are often involved. Several national and international guidelines regarding the investigations and management of IIH have been published in recent years but still there is no consensus about the optimal organization of IIH-care. The objective of this review was to propose and describe a referral pathway and an organization scheme for diagnosis and management of IIH. An extensive search of existing literature was conducted and summarized. In total, 237 IIH-articles were identified and hereof 43 included. The clinical practice in our specialized IIH-clinic is characterized and described. We conclude that an educational campaign involving medical care providers and patients with chronic headaches is necessary. A detailed organizational proposal for a referral pathway and management of IIH patients based on the literature search and our clinical experience from a highly specialized IIH outpatient clinic is suggested and discussed.

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Idiopathic intracranial hypertension: Proposal of a stratification strategy for monitoring risk of disease progression

Cited by 5 publications

References 56 publications

Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

Graphitic Carbon Nitride: A Highly Electroactive Nanomaterial for Environmental and Clinical Sensing

Awareness, Diagnosis and Management of Idiopathic Intracranial Hypertension

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