Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning

Deep learning-based optic disc classification is affected by optic-disc tilt

We aimed to determine the effect of optic disc tilt on deep learning-based optic disc classification. A total of 2507 fundus photographs were acquired from 2236 eyes of 1809 subjects (mean age of 46 years; 53% men). Among all photographs, 1010 (40.3%) had tilted optic discs. Image annotation was performed to label pathologic changes of the optic disc (normal, glaucomatous optic disc changes, disc swelling, and disc pallor). Deep learning-based classification modeling was implemented to develop optic-disc appearance classification models with the photographs of all subjects and those with and without tilted optic discs. Regardless of deep learning algorithms, the classification models showed better overall performance when developed based on data from subjects with non-tilted discs (AUC, 0.988 ± 0.002, 0.991 ± 0.003, and 0.986 ± 0.003 for VGG16, VGG19, and DenseNet121, respectively) than when developed based on data with tilted discs (AUC, 0.924 ± 0.046, 0.928 ± 0.017, and 0.935 ± 0.008). In classification of each pathologic change, non-tilted disc models had better sensitivity and specificity than the tilted disc models. The optic disc appearance classification models developed based all-subject data demonstrated lower accuracy in patients with the appearance of tilted discs than in those with non-tilted discs. Our findings suggested the need to identify and adjust for the effect of optic disc tilt on the optic disc classification algorithm in future development.

Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs

Purpose

To develop a deep learning system to differentiate demyelinating optic neuritis (ON) and non-arteritic anterior ischemic optic neuropathy (NAION) with overlapping clinical profiles at the acute phase.

Methods

We developed a deep learning system (ONION) to distinguish ON from NAION at the acute phase. Color fundus photographs (CFPs) from 871 eyes of 547 patients were included, including 396 ON from 232 patients and 475 NAION from 315 patients. Efficientnet-B0 was used to train the model, and the performance was measured by calculating the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Also, Cohen's kappa coefficients were obtained to compare the system's performance to that of different ophthalmologists.

Results

In the validation data set, the ONION system distinguished between acute ON and NAION achieved the following mean performance: time-consuming (23 s), AUC 0.903 (95% CI 0.827-0.947), sensitivity 0.796 (95% CI 0.704-0.864), and specificity 0.865 (95% CI 0.783-0.920). Testing data set: time-consuming (17 s), AUC 0.902 (95% CI 0.832-0.944), sensitivity 0.814 (95% CI 0.732-0.875), and specificity 0.841 (95% CI 0.762-0.897). The performance (κ = 0.805) was comparable to that of a retinal expert (κ = 0.749) and was better than the other four ophthalmologists (κ = 0.309-0.609).

Conclusion

The ONION system performed satisfactorily distinguishing ON from NAION at the acute phase. It might greatly benefit the challenging differentiation between ON and NAION.

Assistive applications of artificial intelligence in ophthalmology

PURPOSE OF REVIEW

Assistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications.

RECENT FINDINGS

Artificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies.

SUMMARY

Over the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.

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.

Can Tele-Neuro-Ophthalmology Be Useful Beyond the Pandemic?

PURPOSE OF THE REVIEW

Neuro-ophthalmologists rapidly adopted telehealth during the COVID-19 pandemic to minimize disruption to patient care. This article reviews recent research on tele-neuro-ophthalmology adoption, current limitations, and potential use beyond the pandemic. The review considers how digital transformation, including machine learning and augmented reality, may be applied to future iterations of tele-neuro-ophthalmology.

RECENT FINDINGS

Telehealth utilization has been sustained among neuro-ophthalmologists throughout the pandemic. Adoption of tele-neuro-ophthalmology may provide solutions to subspecialty workforce shortage, patient access, physician wellness, and trainee educational needs within the field of neuro-ophthalmology. Digital transformation technologies have the potential to augment tele-neuro-ophthalmology care delivery by providing automated workflow solutions, home-based visual testing and therapies, and trainee education via simulators. Tele-neuro-ophthalmology use has and will continue beyond the COVID-19 pandemic. Digital transformation technologies, when applied to telehealth, will drive and revolutionize the next phase of tele-neuro-ophthalmology adoption and use in the years to come.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

Improving the Telemedicine Evaluation of Patients With Acute Vision Loss: A Call to Eyes

Acute vision loss related to cerebral or retinal ischemia is a time-sensitive emergency with potential treatment options including intravenous or intraarterial thrombolysis and mechanical thrombectomy. However, patients either present in delayed fashion or present to an emergency department that lacks the subspecialty expertise to recognize and treat these conditions in a timely fashion. Moreover, healthcare systems in the United States are becoming increasingly reliant on telestroke and teleneurology services for acute neurologic care, making accurate diagnosis of acute vision loss even more challenging due to critical limitations to the remote video evaluation, including the inability to perform routine ophthalmoscopy. The COVID-19 pandemic has led to a greater reliance on telemedicine services and helped to accelerate the development of novel tools and care pathways to improve remote ophthalmologic evaluation, but these tools have yet to be adapted for use in the remote evaluation of acute vision loss. Permanent vision loss can be disabling for patients and efforts must be made to increase and improve early diagnosis and management. Herein, the authors outline the importance of improving acute ophthalmologic diagnosis, outline key limitations and barriers to the current video-based teleneurology assessments, highlight opportunities to leverage new tools to enhance the remote assessment of vision loss, and propose new avenues to improve access to emergent ophthalmology subspeciality.

The Ethical and Societal Considerations for the Rise of Artificial Intelligence and Big Data in Ophthalmology

Medical specialties with access to a large amount of imaging data, such as ophthalmology, have been at the forefront of the artificial intelligence (AI) revolution in medicine, driven by deep learning (DL) and big data. With the rise of AI and big data, there has also been increasing concern on the issues of bias and privacy, which can be partially addressed by low-shot learning, generative DL, federated learning and a "model-to-data" approach, as demonstrated by various groups of investigators in ophthalmology. However, to adequately tackle the ethical and societal challenges associated with the rise of AI in ophthalmology, a more comprehensive approach is preferable. Specifically, AI should be viewed as sociotechnical, meaning this technology shapes, and is shaped by social phenomena.

Value of Combining Optical Coherence Tomography with Fundus Photography in Screening Retinopathy in Patients with High Myopia

Objective

To explore the value of combining optical coherence tomography (OCT) with fundus photography in screening retinopathy in patients with high myopia.

Methods

By means of retrospective study, 40 high myopia patients with retinopathy treated in our hospital from January 2020 to January 2021 were selected as the study group, and 40 healthy individuals in the same period were included in the control group. All patients received traditional ophthalmic examination, and accepted fundus fluorescence imaging, OCT, and fundus photography examination step by step by the same operator. After that, three physicians read the slides by the double blind method, and took the results of fundus fluorescence imaging as the gold standard to analyze the diagnostic efficacy of OCT, fundus photography and their combination.

Results

The clinical data and examination results showed that no statistical differences in general data including patients' mean age, gender ratio, and educational degree between the study group and the control group were observed (P > 0.05), and the nerve thickness above/below the optic disk and temporal/nasal nerve thickness of the optic disk of the study group were significantly different from those of the control group (P < 0.001); the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy rate of diagnosis of combining OCT with fundus photography were respectively 95.0%, 97.5%, 97.4%, 95.1%, and 96.3%, which were significantly higher than OCT or fundus photography alone (P < 0.05); and for combined examination, AUC (95%CI) = 0.963 (0.000–1.000).

Conclusion

Combining OCT with fundus photography can effectively identify high myopia patients with retinopathy, which is conducive to improving clinical positive ratio and providing objective basis for treatment.

Artificial Intelligence Meets Neuro-Ophthalmology

ABSTRACT

Recent advances in artificial intelligence have provided ophthalmologists with fast, accurate, and automated means for diagnosing and treating ocular conditions, paving the way to a modern and scalable eye care system. Compared to other ophthalmic disciplines, neuro-ophthalmology has, until recently, not benefitted from significant advances in the area of artificial intelligence. In this narrative review, we summarize and discuss recent advancements utilizing artificial intelligence for the detection of structural and functional optic nerve head abnormalities, and ocular movement disorders in neuro-ophthalmology.

Papilledema: A review of etiology, pathophysiology, diagnosis, and management

Papilledema is optic nerve head edema secondary to raised intracranial pressure (ICP). It is distinct from other causes of optic disk edema in that visual function is usually normal in the acute phase. Papilledema is caused by transmission of elevated ICP to the subarachnoid space surrounding the optic nerve that hinders axoplasmic transport within ganglion cell axons. There is ongoing controversy as to whether axoplasmic flow stasis is produced by physical compression of axons or microvascular ischemia. The most common cause of papilledema, especially in patients under the age of 50, is idiopathic intracranial hypertension (IIH); however, conditions that decrease cerebrospinal fluid (CSF) outflow by either causing CSF derangements or mechanically blocking CSF outflow channels, and rarely conditions that increase CSF production, can be the culprit. When papilledema is suspected clinically, blood pressure should be measured, and pseudopapilledema should be ruled out. Magnetic resonance imaging of the brain and orbits with venography sequences is the preferred neuroimaging modality that should be performed next to look for indirect imaging signs of increased ICP and to rule out nonidiopathic causes. Lumbar puncture with measurement of opening pressure and evaluation of CSF composition should then be performed. In patients not in a typical demographic group for IIH, further investigations should be conducted to assess for underlying causes of increased ICP. Magnetic resonance imaging of the neck and spine, magnetic resonance angiography of the brain, computed tomography of the chest, complete blood count, and creatinine testing should be able to identify most secondary causes of intracranial hypertension. Treatment for patients with papilledema should be targeted toward the underlying etiology. Most patients with IIH respond to weight loss and oral acetazolamide. For patients with decreased central acuity and constricted visual fields at presentation, as well as patients who do not respond to treatment with acetazolamide, surgical treatments should be considered, with ventriculoperitoneal shunting being the typical procedure of choice.