Enhanced Depth Imaging Optical Coherence Tomography of Optic Nerve Head Drusen in Children

Application of Optical Coherence Tomography Angiography in True and Pseudo-Optic Disc Swelling

Purpose

We evaluated the optic disc microvasculature in healthy subjects and patients with optic nerve head drusen (ONHD), active papilledema, and acute nonarteritic anterior ischemic optic neuropathy (NAION) using optical coherence tomography angiography (OCTA).

Methods

This prospective, comparative case series included sixteen eyes with ONHD, thirty-one eyes with active papilledema, sixteen eyes with acute NAION, and thirty-two healthy eyes. The Optovue AngioVue OCT and OCTA Imaging System recorded peripapillary retinal nerve fiber layer (RNFL) thickness and vessel density maps from the radial peripapillary capillary (RPC) slab.

Results

Average RNFL thicknesses were greater in eyes with ONHD, papilledema, and NAION compared to control eyes (all Ps < 0.001), but this parameter did not differ among patient groups. The mean peripapillary vessel density did not differ between the ONHD and control groups (P=1.000), nor between the NAION and papilledema groups (P=0.216). However, this value in the ONHD and control groups was significantly higher than in the NAION and papilledema groups (all Ps < 0.05).

Conclusion

RPC density is influenced during the progression of conditions such as ONHD, papilledema, and NAION. Although a decrease in vessel density values has been observed in cases of true disc edema, further research is necessary to assess the potential of OCTA in differentiating between true and pseudo-optic disc edema.

Diagnostic dilemma of papilledema and pseudopapilledema

BACKGROUND

Papilledema is the optic disc swelling caused by increased intracranial pressure (ICP) that can damage the optic nerve and cause subsequent vision loss. Pseudopapilledema refers to optic disc elevation without peripapillary fluid that can arise from several optic disc disorders, with optic disc drusen (ODD) being the most frequent cause. Occasionally, pseudopapilledema patients are mistakenly diagnosed as papilledema, leading to the possibility of unneeded procedures. We aim to thoroughly examine the most current evidence on papilledema and pseudopapilledema causes and several methods for distinguishing between both conditions.

METHODS

An extensive literature search was conducted on electronic databases including PubMed and google scholar using keywords that were relevant to the assessed pathologies. Data were collected and then summarized in comprehensive form.

RESULTS

Various techniques are employed to distinguish between papilledema and pseudopapilledema. These techniques include Fundus fluorescein angiography, optical coherence tomography, ultrasonography, and magnetic resonance imaging. Lumbar puncture and other invasive procedures may be needed if results are suspicious.

CONCLUSION

Papilledema is a sight-threatening condition that may lead to visual affection. Many disc conditions may mimic papilledema. Accordingly, differentiation between papilledema and pseudopailledema is crucial and can be conducted through many modalities.

Discriminating Healthy Optic Discs and Visible Optic Disc Drusen on Fundus Autofluorescence and Color Fundus Photography Using Deep Learning-A Pilot Study

The aim of this study was to use deep learning based on a deep convolutional neural network (DCNN) for automated image classification of healthy optic discs (OD) and visible optic disc drusen (ODD) on fundus autofluorescence (FAF) and color fundus photography (CFP). In this study, a total of 400 FAF and CFP images of patients with ODD and healthy controls were used. A pre-trained multi-layer Deep Convolutional Neural Network (DCNN) was trained and validated independently on FAF and CFP images. Training and validation accuracy and cross-entropy were recorded. Both generated DCNN classifiers were tested with 40 FAF and CFP images (20 ODD and 20 controls). After the repetition of 1000 training cycles, the training accuracy was 100%, the validation accuracy was 92% (CFP) and 96% (FAF), respectively. The cross-entropy was 0.04 (CFP) and 0.15 (FAF). The sensitivity, specificity, and accuracy of the DCNN for classification of FAF images was 100%. For the DCNN used to identify ODD on color fundus photographs, sensitivity was 85%, specificity 100%, and accuracy 92.5%. Differentiation between healthy controls and ODD on CFP and FAF images was possible with high specificity and sensitivity using a deep learning approach.

Discriminating Between Papilledema and Optic Disc Drusen Using 3D Structural Analysis of the Optic Nerve Head

BACKGROUND AND OBJECTIVES

The distinction of papilledema from other optic nerve head (ONH) lesions mimicking papilledema, such as optic disc drusen (ODD), can be difficult in clinical practice. We aimed the following: (1) to develop a deep learning algorithm to automatically identify major structures of the ONH in 3-dimensional (3D) optical coherence tomography (OCT) scans and (2) to exploit such information to robustly differentiate among ODD, papilledema, and healthy ONHs.

METHODS

This was a cross-sectional comparative study of patients from 3 sites (Singapore, Denmark, and Australia) with confirmed ODD, those with papilledema due to raised intracranial pressure, and healthy controls. Raster scans of the ONH were acquired using OCT imaging and then processed to improve deep-tissue visibility. First, a deep learning algorithm was developed to identify major ONH tissues and ODD regions. The performance of our algorithm was assessed using the Dice coefficient. Second, a classification algorithm (random forest) was designed to perform 3-class classifications (1: ODD, 2: papilledema, and 3: healthy ONHs) strictly from their drusen and prelamina swelling scores (calculated from the segmentations). To assess performance, we reported the area under the receiver operating characteristic curve for each class.

RESULTS

A total of 241 patients (256 imaged ONHs, including 105 ODD, 51 papilledema, and 100 healthy ONHs) were retrospectively included in this study. Using OCT images of the ONH, our segmentation algorithm was able to isolate neural and connective tissues and ODD regions/conglomerates whenever present. This was confirmed by an averaged Dice coefficient of 0.93 ± 0.03 on the test set, corresponding to good segmentation performance. Classification was achieved with high AUCs, that is, 0.99 ± 0.001 for the detection of ODD, 0.99 ± 0.005 for the detection of papilledema, and 0.98 ± 0.01 for the detection of healthy ONHs.

DISCUSSION

Our artificial intelligence approach can discriminate ODD from papilledema, strictly using a single OCT scan of the ONH. Our classification performance was very good in the studied population, with the caveat that validation in a much larger population is warranted. Our approach may have the potential to establish OCT imaging as one of the mainstays of diagnostic imaging for ONH disorders in neuro-ophthalmology, in addition to fundus photography.

Young Adults With Anterior Ischemic Optic Neuropathy: A Multicenter Optic Disc Drusen Study

PURPOSE

Optic disc drusen (ODD), present in 2% of the general population, have occasionally been reported in patients with nonarteritic anterior ischemic optic neuropathy (NA-AION). The purpose of this study was to examine the prevalence of ODD in young patients with NA-AION.

DESIGN

Retrospective, cross-sectional multicenter study.

METHODS

All patients with NA-AION 50 years old or younger, seen in neuro-ophthalmology clinics of the international ODDS (Optic Disc Drusen Studies) Consortium between April 1, 2017, and March 31, 2019, were identified. Patients were included if ODD were diagnosed by any method, or if ODD were excluded by enhanced-depth imaging optical coherence tomography (EDI-OCT) using ODDS Consortium guidelines. NA-AION eyes with ODD were termed "ODD-AION"; those without were termed "NODD-AION".

RESULTS

A total of 65 patients (127 eyes) with NA-AION were included (mean 41 years old). Of the 74 eyes with NA-AION, 51% had ODD-AION, whereas 43% of fellow eyes without NA-AION had ODD (P = .36). No significant differences were found between ODD-AION and NODD-AION eyes in terms of Snellen best-corrected VA or perimetric mean deviation. According to EDI-OCT results, 28% of eyes with NODD-AION had peripapillary hyperreflective ovoid mass-like structures (PHOMS); 7% had hyperreflective lines, whereas 54% with ODD-AION had PHOMS; and 66% had hyperreflective lines (P = .006 and P < .001, respectively).

CONCLUSIONS

Most of these young NA-AION patients had ODD. This indicates that ODD may be an independent risk factor for the development of NA-AION, at least in younger patients. This study suggests ODD-AION be recognized as a novel diagnosis.