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

A Deep Learning Approach for Accurate Discrimination Between Optic Disc Drusen and Papilledema on Fundus Photographs

BACKGROUND

Optic disc drusen (ODD) represent an important differential diagnosis of papilledema caused by intracranial hypertension, but their distinction may be difficult in clinical practice. The aim of this study was to train, validate, and test a dedicated deep learning system (DLS) for binary classification of ODD vs papilledema (including various subgroups within each category), on conventional mydriatic digital ocular fundus photographs collected in a large international multiethnic population.

METHODS

This retrospective study included 4,508 color fundus images in 2,180 patients from 30 neuro-ophthalmology centers (19 countries) participating in the Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) Group. For training and internal validation, we used 857 ODD images and 3,230 papilledema images, in 1,959 patients. External testing was performed on an independent data set (221 patients), including 207 images with ODD (96 visible and 111 buried), provided by 3 centers of the Optic Disc Drusen Studies Consortium, and 214 images of papilledema (92 mild-to-moderate and 122 severe) from a previously validated study.

RESULTS

The DLS could accurately distinguish between all ODD and papilledema (all severities included): area under the receiver operating characteristic curve (AUC) 0.97 (95% confidence interval [CI], 0.96-0.98), accuracy 90.5% (95% CI, 88.0%-92.9%), sensitivity 86.0% (95% CI, 82.1%-90.1%), and specificity 94.9% (95% CI, 92.3%-97.6%). The performance of the DLS remained high for discrimination of buried ODD from mild-to-moderate papilledema: AUC 0.93 (95% CI, 0.90-0.96), accuracy 84.2% (95% CI, 80.2%-88.6%), sensitivity 78.4% (95% CI, 72.2%-84.7%), and specificity 91.3% (95% CI, 87.0%-96.4%).

CONCLUSIONS

A dedicated DLS can accurately distinguish between ODD and papilledema caused by intracranial hypertension, even when considering buried ODD vs mild-to-moderate papilledema.

Recent advances in neuro-ophthalmology

This review article represents a collaborative effort across continents, bringing together the latest developments in neuro-ophthalmology with a focus on innovative diagnostic and therapeutic modalities that are shaping the future of the field. Among the most significant advancements is the rise of optical coherence tomography (OCT), now recognized as an indispensable tool in neuro-ophthalmological research, providing unparalleled insights into optic nerve and central nervous system pathologies. Gene therapy, particularly for conditions such as Leber's hereditary optic neuropathy, marks a new frontier in personalized medicine, offering hope for previously untreatable conditions. The article also examines the transformative role of telemedicine and artificial intelligence (AI) in clinical practice, which are revolutionizing patient care and enhancing diagnostic precision. Furthermore, it highlights the impact of novel serological biomarkers on the understanding and management of immune-mediated optic neuritis, and discusses the introduction of new therapeutic agents like Tocilizumab and Teprotumumab, which are redefining treatment paradigms. Collectively, these advancements reflect the profound influence of modern medicine on neuro-ophthalmology, paving the way for improved patient outcomes and fostering new avenues for research and clinical practice.

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities

BACKGROUND

Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease.

EVIDENCE ACQUISITION

Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI.

RESULTS

This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice.

CONCLUSIONS

We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.

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.

Application of novel non-invasive ophthalmic imaging to visualize peripapillary wrinkles, retinal folds and peripapillary hyperreflective ovoid mass-like structures associated with elevated intracranial pressure

Background

Elevated intracranial pressure (ICP) is a serious and potentially life-threatening condition, for which clinically useful non-invasive measures have been elusive, in some cases due to their inadequate sensitivity and specificity. Our aim was to evaluate novel non-invasive ophthalmic imaging of selected pathological features seen in elevated ICP, namely peripapillary hyperreflective ovoid mass-like structures (PHOMS), peripapillary wrinkles (PPW) and retinal folds (RF) as potential biomarkers of elevated ICP.

Methods

This single-center pilot study included subjects with untreated or incompletely treated high ICP. The retinas of these subjects were evaluated with averaged en-face optical coherence tomography (OCT), OCT retinal cross-sections (OCT B-scans), adaptive optics scanning light ophthalmoscopy (AOSLO), and fundus photos.

Results

Seven subjects were included in the study. 6 subjects with high ICP (5 idiopathic intracranial hypertension, 1 medication induced, 30.8 ± 8.6 years, 75% female, 5 with papilledema) and 1 control (20-25 years) were included. PHOMS, PPW and RF were present in all subjects with papilledema, but neither in the high ICP subject without papilledema nor in the control subject. Averaged en-face OCT scans and AOSLO were more sensitive for PPW and RF than OCT B-scans and commercial fundus photos.

Conclusion

PPW, RF and PHOMS volume have potential as non-invasive biomarkers of ICP. Novel imaging modalities may improve sensitivity. However, lack of automated image acquisition and processing limits current widespread adoption in clinical settings. Further research is needed to validate these structures as biomarkers for elevated ICP and improve clinical utility.

Early Detection of Optic Nerve Changes on Optical Coherence Tomography Using Deep Learning for Risk-Stratification of Papilledema and Glaucoma

BACKGROUND

The use of artificial intelligence is becoming more prevalence in medicine with numerous successful examples in ophthalmology. However, much of the work has been focused on replicating the works of ophthalmologists. Given the analytical potentials of artificial intelligence, it is plausible that artificial intelligence can detect microfeatures not readily distinguished by humans. In this study, we tested the potential for artificial intelligence to detect early optic coherence tomography changes to predict progression toward papilledema or glaucoma when no significant changes are detected on optical coherence tomography by clinicians.

METHODS

Prediagnostic optical coherence tomography of patients who developed papilledema (n = 93, eyes = 166) and glaucoma (n = 187, eyes = 327) were collected. Given discrepancy in average cup-to-disc ratios of the experimental groups, control groups for papilledema (n = 254, eyes = 379) and glaucoma (n = 441, eyes = 739) are matched by cup-to-disc ratio. Publicly available Visual Geometry Group-19 model is retrained using each experimental group and its respective control group to predict progression to papilledema or glaucoma. Images used for training include retinal nerve fiber layer thickness map, extracted vertical tomogram, ganglion cell thickness map, and ILM-RPE thickness map.

RESULTS

Trained model was able to predict progression to papilledema with a precision of 0.714 and a recall of 0.769 when trained with retinal nerve fiber layer thickness map, but not other image types. However, trained model was able to predict progression to glaucoma with a precision of 0.682 and recall of 0.857 when trained with extracted vertical tomogram, but not other image types. Area under precision-recall curve of 0.826 and 0.785 were achieved for papilledema and glaucoma models, respectively.

CONCLUSIONS

Computational and analytical power of computers have become an invaluable part of our lives and research endeavors. Our proof-of-concept study showed that artificial intelligence (AI) algorithms have the potential to detect early changes on optical coherence tomography for prediction of progression that is not readily observed by clinicians. Further research may help establish possible AI models that can assist with early diagnosis or risk stratification in ophthalmology.

Recent advances in the use of optical coherence tomography in neuro-ophthalmology: A review

Optical coherence tomography (OCT) is an in vivo imaging modality that provides non-invasive, high resolution and fast cross-sectional images of the optic nerve head, retina and choroid. OCT angiography (OCTA) is an emerging tool. It is a non-invasive, dye-free imaging approach of visualising the microvasculature of the retina and choroid by employing motion contrast imaging for blood flow detection and is gradually receiving attention for its potential roles in various neuro-ophthalmic and retinal conditions. We will review the clinical utility of the OCT in the management of various common neuro-ophthalmic and neurological disorders. We also review some of the OCTA research findings in these conditions. Finally, we will discuss the limitations of OCT as well as introduce other emerging technologies.

Concomitant optic disk drusen and papilledema due to idiopathic intracranial hypertension in a pediatric cohort

BACKGROUND

Optic disk drusen (ODD) in pediatric patients typically presents with pseudopapilledema. Diagnosing concomitant papilledema due to idiopathic intracranial hypertension (IIH) in these patients can be challenging. The purpose of this study was to evaluate the incidence and clinical features of papilledema due to IIH among pediatric patients with a new diagnosis of ODD and to discuss the clinical and paraclinical findings that helped diagnose this group.

METHODS

The medical records of children <15 years of age with ODD confirmed by B-scan ultrasound at their first visit over a 4-year period (2019-2022) were reviewed retrospectively. Patients with concurrent IIH were identified, and the demographic and clinical characteristics were reviewed.

RESULTS

A total of 83 children with confirmed ODD at the initial presentation were included, of whom 4 (4.8%) were diagnosed with concomitant IIH. Patients ranged in age from 7 to 15 years; 3 of the 4 were female, and 3 had IIH-related symptoms at presentation (1 was asymptomatic). None of the 4 patients had papilledema greater than Frisen grade 2.

CONCLUSIONS

We recommend that clinicians review pertinent IIH symptoms and risk factors in children with ODD and follow the standard workup for IIH in suspicious cases. In asymptomatic patients with a new diagnosis of ODD, we recommend obtaining a follow-up optic nerve evaluation and optical coherence tomography scan to detect any significant interval change that might serve as a possible indicator of concomitant papilledema.

Through the eyes into the brain, using artificial intelligence

Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice. Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema

Diagnosis of Optic Disc Oedema: Fundus Features, Ocular Imaging Findings, and Artificial Intelligence

Optic disc swelling is a manifestation of a broad range of processes affecting the optic nerve head and/or the anterior segment of the optic nerve. Accurately diagnosing optic disc oedema, grading its severity, and recognising its cause, is crucial in order to treat patients in a timely manner and limit vision loss. Some ocular fundus features, in light of a patient's history and visual symptoms, may suggest a specific mechanism or aetiology of the visible disc oedema, but current criteria can at most enable an educated guess as to the most likely cause. In many cases only the clinical evolution and ancillary testing can inform the exact diagnosis. The development of ocular fundus imaging, including colour fundus photography, fluorescein angiography, optical coherence tomography, and multimodal imaging, has provided assistance in quantifying swelling, distinguishing true optic disc oedema from pseudo-optic disc oedema, and differentiating among the numerous causes of acute optic disc oedema. However, the diagnosis of disc oedema is often delayed or not made in busy emergency departments and outpatient neurology clinics. Indeed, most non-eye care providers are not able to accurately perform ocular fundus examination, increasing the risk of diagnostic errors in acute neurological settings. The implementation of non-mydriatic fundus photography and artificial intelligence technology in the diagnostic process addresses these important gaps in clinical practice.