Ophthalmic Imaging Modalities Research Articles

A comparative evaluation of deep learning approaches for ophthalmology

There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This allows ophthalmic researchers and practitioners to independently perform various deep-learning tasks. With the advancement in artificial intelligence (AI) and in the field of imaging, the choice of the most appropriate AI architecture for different tasks will vary greatly. The best-performing AI-dataset combination will depend on the specific problem that needs to be solved and the type of data available. The article discusses different machine learning models and deep learning architectures currently used for various ophthalmic imaging modalities and for different machine learning tasks. It also proposes the most appropriate models based on accuracy and other important factors such as training time, the ability to deploy the model on clinical devices/smartphones, heatmaps that enhance the self-explanatory nature of classification decisions, and the ability to train/adapt on small image datasets to determine if further data collection is worthwhile. The article extensively reviews the existing state-of-the-art AI methods focused on useful machine-learning applications for ophthalmology. It estimates their performance and viability through training and evaluating architectures with different public and private image datasets of different modalities, such as full-color retinal images, OCT images, and 3D OCT scans. The article is expected to benefit the readers by enriching their knowledge of artificial intelligence applied to ophthalmology.

An Explainable Artificial Intelligence-Based Robustness Optimization Approach for Age-Related Macular Degeneration Detection Based on Medical IOT Systems

AI-based models have shown promising results in diagnosing eye diseases based on multi-sources of data collected from medical IOT systems. However, there are concerns regarding their generalization and robustness, as these methods are prone to overfitting specific datasets. The development of Explainable Artificial Intelligence (XAI) techniques has addressed the black-box problem of machine learning and deep learning models, which can enhance interpretability and trustworthiness and optimize their performance in the real world. Age-related macular degeneration (AMD) is currently the primary cause of vision loss among elderly individuals. In this study, XAI methods were applied to detect AMD using various ophthalmic imaging modalities collected from medical IOT systems, such as colorful fundus photography (CFP), optical coherence tomography (OCT), ultra-wide fundus (UWF) images, and fluorescein angiography fundus (FAF). An optimized deep learning (DL) model and novel AMD identification systems were proposed based on the insights extracted by XAI. The findings of this study demonstrate that XAI not only has the potential to improve the transparency, reliability, and trustworthiness of AI models for ophthalmic applications, but it also has significant advantages for enhancing the robustness performance of these models. XAI could play a crucial role in promoting intelligent ophthalmology and be one of the most important techniques for evaluating and enhancing ophthalmic AI systems.

Ophthalmic imaging in abusive head trauma

BackgroundOphthalmic imaging plays an increasingly important role the evaluation of abusive head trauma, however these imaging modalities may be unfamiliar to non-ophthalmologists. ObjectiveTo provide pediatricians and child abuse pediatric professionals with background on ophthalmic imaging techniques in the context of suspected abuse, as well as information on commercial options and costs for those interested in augmenting their ophthalmic imaging capabilities. MethodsWe performed a review of the ophthalmic imaging literature for fundus photography, ocular coherence tomography, fluorescein angiography, ocular ultrasound, computed tomography, magnetic resonance imaging and postmortem imaging. We also contacted individual vendors for equipment pricing information. ResultsFor each ophthalmic imaging modality, we demonstrate its role in the evaluation of abusive head trauma including indications, potential findings, sensitivity and specificity of findings for abuse, and commercial options. ConclusionsOphthalmic imaging is an important supportive component of the evaluation for abusive head trauma. When used in conjunction with clinical examination, ophthalmic imaging can improve diagnostic accuracy, support documentation, and possibly improve communication in medicolegal contexts.

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images.

Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.

Myopic macular diseases: A review.

Recent evidence has demonstrated that the global public health burden of myopia is rising rapidly. Highly myopic eyes are associated with increased frequency of eye disorders that can lead to irreversible visual impairment. With recent technological advancement in ophthalmic imaging modalities, various macular complications associated with pathologic myopia are being elucidated. The development and progression of myopic chorioretinal atrophy, myopic macular neovascularization, myopic traction maculopathy and dome-shaped macula are vision-threatening myopic macular diseases. In order to overcome the challenges in managing patients with pathologic myopia, it is important to have a complete understanding in the natural course of these myopic macular diseases. Standardising the classification criteria of pathologic myopia is essential for enhancing clinical surveillance. Personalised pharmaceutical therapy and surgical interventions will help to optimise the treatment outcomes in patients suffering from these myopic macular diseases.

Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities

Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities

Geographic atrophy: where we are now and where we are going

Age-related macular degeneration (AMD) affects a significant percentage of the elderly population and end-stage disease classified by either geographic atrophy (GA) or neovascular AMD (nvAMD) is one of the leading causes of vision loss worldwide. Despite the fact that there are currently treatments for nvAMD, there are no treatments in practice to prevent disease onset or progression of GA. This topic is at the forefront of ophthalmic research demonstrated by the recent advances in disease characterization, genetic and environmental risk factor classification, biomarker discovery and mechanism of pathogenesis categorization. There are also numerous clinical treatment trials underway, targeting proposed pathways and biomarkers associated with GA that are promising. With several clinical trials of potential treatments underway and numerous recent publications on disease diagnosis and classification, the understanding of GA pathogenesis has increased substantially. Although the exact mechanism of pathology is still elusive, recent literature has highlighted the utilization of current and new ophthalmic imaging modalities and discovery of objective and functional markers that can lead to earlier diagnosis and treatment. Herein, we will provide an overview and discussion of the current status of GA including advances in mechanism of pathogenesis, diagnosis, classification and current treatment modalities.

Clinical 3D Imaging of the Anterior Segment With Ultrasound Biomicroscopy.

PurposeUltrasound biomicroscopy (UBM) is an important ophthalmic imaging modality due to its ability to see behind pigmented iris and to visualize anterior chamber when the eye's transparency is compromised. We created a three-dimensional UBM (3D-UBM) system and acquired example images to illustrate its potential.MethodsA commercial 50-MHz two-dimensional UBM (2D-UBM) system was attached to a precision translation stage and translated across the eye to acquire an image volume. The stage was mounted on a surgical microscope, which enabled safe, stable positioning. Image processing steps included image alignment, noise reduction, and calibration. 3D visualization included alignment of the optic axis, multiplanar reformatting at arbitrary orientations, and volume rendering with optimized transfer functions. Scans were performed on cadaver and rabbit eyes.Results3D-UBM allowed visualization of the anterior segment tissues within a 3D anatomical context, unlike 2D-UBM. En face views and interactive slicer operations suggested an ability to plan and assess treatments, including lens placement and microcatheter cannulation of Schlemm's canal. Interactive software allowed us to make accurate measurements of tissue structures (e.g., iridocorneal angles, cyst volumes). In addition, unique measurements of ciliary tissues included single ciliary process volumes of 0.234 ± 0.093 mm3 with surface areas of 3.02 ± 1.07 mm2 and ciliary muscle volume of 67.87 mm3.Conclusions3D-UBM imaging of the anterior segment can be used to enable unique visualization and quantification of anterior segment structures.Translational Relevance3D-UBM provides informative 3D imaging of tissues in the eye that are invisible to light to potentially provide physicians with improved diagnosis, treatment planning, and treatment assessment as compared to conventional 2D-UBM.

Retinoblastoma: What the Neuroradiologist Needs to Know.

Retinoblastoma is the most common primary intraocular tumor of childhood. Accurate diagnosis at an early stage is important to maximize patient survival, globe salvage, and visual acuity. Management of retinoblastoma is individualized based on the presenting clinical and imaging features of the tumor, and a multidisciplinary team is required to optimize patient outcomes. The neuroradiologist is a key member of the retinoblastoma care team and should be familiar with characteristic diagnostic and prognostic imaging features of this disease. Furthermore, with the adoption of intra-arterial chemotherapy as a standard of care option for globe salvage therapy in many centers, the interventional neuroradiologist may play an active role in retinoblastoma treatment. In this review, we discuss the clinical presentation of retinoblastoma, ophthalmic imaging modalities, neuroradiology imaging features, and current treatment options.

Gold nanoparticles to enhance ophthalmic imaging.

The use of gold nanoparticles as diagnostic tools is burgeoning, especially in the cancer community with a focus on theranostic applications to both cancer diagnosis and treatment. Gold nanoparticles have also demonstrated great potential for use in diagnostic and therapeutic approaches in ophthalmology. Although many ophthalmic imaging modalities are available, there is still a considerable unmet need, in particular for ophthalmic molecular imaging for the early detection of eye disease before morphological changes are more grossly visible. An understanding of how gold nanoparticles are leveraged in other fields could inform new ways they could be utilized in ophthalmology. In this paper, we review current ophthalmic imaging techniques and then identify optical coherence tomography (OCT) and photoacoustic imaging (PAI) as the most promising technologies amenable to the use of gold nanoparticles for molecular imaging. Within this context, the development of gold nanoparticles as OCT and PAI contrast agents are reviewed, with the most recent developments described in detail.

Correction propagation for user-assisted optical coherence tomography segmentation: general framework and application to Bruch's membrane segmentation.

Optical coherence tomography (OCT) is a commonly used ophthalmic imaging modality. While OCT has traditionally been viewed cross-sectionally (i.e., as a sequence of B-scans), higher A-scan rates have increased interest in en face OCT visualization and analysis. The recent clinical introduction of OCT angiography (OCTA) has further spurred this interest, with chorioretinal OCTA being predominantly displayed via en face projections. Although en face visualization and quantitation are natural for many retinal features (e.g., drusen and vasculature), it requires segmentation. Because manual segmentation of volumetric OCT data is prohibitively laborious in many settings, there has been significant research and commercial interest in developing automatic segmentation algorithms. While these algorithms have achieved impressive results, the variability of image qualities and the variety of ocular pathologies cause even the most robust automatic segmentation algorithms to err. In this study, we develop a user-assisted segmentation approach, complementary to fully-automatic methods, wherein correction propagation is used to reduce the burden of manually correcting automatic segmentations. The approach is evaluated for Bruch's membrane segmentation in eyes with advanced age-related macular degeneration.

Artificial intelligence and its applications in ophthalmology

The term artificial intelligence (AI) was proposed in 1956 by Dartmouth scholar John McCarthy, which refers to hardware or software that exhibits behavior which appears intelligent.1 During recent times, AI gained immense popularity as new algorithms, specialized hardware, huge data and cloud-based services were developed. Machine learning (ML), a subset of AI, originated in 1980 and is defined as a set of methods that automatically detect patterns in data and then incorporate this information to predict future data under uncertain conditions. Another escalating technology of ML called Deep learning (DL), launched in 2000s, is an escalating technology of ML and has revolutionized the world of AI. These technologies are powerful tools utilized by modern society for objects' recognition in images, real-time languages' translation, device manipulation via speech (such as Apple's Siri®, Amazon’s Alexa®, Microsoft’s Cortana®, etc.). The steps for AI model include preprocessing image data, train, validate and test the model, and evaluate the trained model's performance. To increase AI prediction efficiency, raw data need to be preprocessed. Data collected from different sources needs to be integrated and the most relevant features selected and extracted to improve the learning process performance. Data set is randomly partitioned into two independent subsets, one is for modeling and the other is for testing. The test set is used to evaluate the final performance of the trained model. The area under receiver operating characteristic curves (AUC) is most used evaluation metrics for quantitative assessment of a model in AI diagnosis. The AUCs effective models range from 0.5 to 1; higher the value of AUC, better the performance of the model.2 In the medical field, AI gained popularity by visualization of input images of highly potential abnormal sites which can be reviewed and analyzed in future.
 AI and DL algorithms or systems are also widely used in field of ophthalmology. More intensively studied fields are diabetic retinopathy, age related macular degeneration, and cataract and glaucoma. Various ophthalmic imaging modalities used for AI diagnosis include fundus image, optical coherence tomography (OCT), ocular ultrasound, slit-lamp image and visual field. Diabetic retinopathy (DR), a diabetic complication, is a vasculopathy that affects one-third of diabetic patients leading to irreversible blindness. AI has been in use to predict DR risk and its progression. Gulshan and colleague were the first to report the application of DL for DR identification.3 They used large fundus image data sets in supervised manner for DR detection. Other studies applied DL to identify and stage DR. DL-based computer-aided system was introduced to detect DR through OCT images, achieving a specificity of 0.98.4 A computer-aided diagnostic (CAD) system based on CML algorithms using optical coherence tomography angiography images to automatically diagnose non-proliferative DR (NPDR) also achieved high accuracy and AUC.5 Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among old people in the developed world. ML algorithms are being used to identify AMD lesions and prompt early treatment with accuracy usually over 80%.6 Using ML to predict treatment of retinal neovascularity in AMD and DR by anti-vascular endothelial growth factor (Anti VEGF) injection requirements can manage patients' economic burden and resource management. ML algorithms have been applied to diagnose and grade cataract using fundus images, ultrasounds images, and visible wavelength eye images.7 Glaucoma is the third largest sight-threatening eye disease around the world. Glaucoma patients suffered from high intraocular pressure, damage of the optic nerve head, retina nerve fiber layer defect, and gradual vision loss. Studies using DL methods to diagnose glaucoma are few. So far, fundus images and wide-field OCT scans have all been used to construct DL-based glaucomatous diagnostic models. Mostly, the DL-based methods show excellent results.8
 In this era of “evidence-based medicine,” clinicians and patients find it difficult to trust a mysterious machine to diagnose yet cannot provide explanations of why the patient has certain disease. In future, advanced AI interpreters will be launched which will contribute significantly to revolutionize current disease diagnostic pattern.

Visualization of Schlemm's canal from 3D reconstruction of UMB images

Ultrasound biomicroscopy (UBM) is widely used by ophthalmologists for visualizing anterior segment structures. Compared with other ophthalmic imaging modalities, UBM excels in its ability to penetrate opaque tissue and visualize structures such as Schlemm's canal which cannot be seen through the lens.

Corneal Nerve Fractal Dimension: A Novel Corneal Nerve Metric for the Diagnosis of Diabetic Sensorimotor Polyneuropathy.

ObjectiveCorneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, is a noninvasive and objective imaging biomarker for identifying small nerve fiber damage. We have evaluated the diagnostic performance of previously established CCM parameters to a novel automated measure of corneal nerve complexity called the corneal nerve fiber fractal dimension (ACNFrD).MethodsA total of 176 subjects (84 controls and 92 patients with type 1 diabetes) with and without diabetic sensorimotor polyneuropathy (DSPN) underwent CCM. Fractal dimension analysis was performed on CCM images using purpose-built corneal nerve analysis software, and compared with previously established manual and automated corneal nerve fiber measurements.ResultsManual and automated subbasal corneal nerve fiber density (CNFD) (P < 0.0001), length (CNFL) (P < 0.0001), branch density (CNBD) (P < 0.05), and ACNFrD (P < 0.0001) were significantly reduced in patients with DSPN compared to patients without DSPN. The areas under the receiver operating characteristic curves for identifying DSPN were comparable: 0.77 for automated CNFD, 0.74 for automated CNFL, 0.69 for automated CNBD, and 0.74 for automated ACNFrD.ConclusionsACNFrD shows comparable diagnostic efficiency to identify diabetic patients with and without DSPN.

Diagnosing Diabetic Neuropathy: Something Old, Something New.

There are potentially many ways of assessing diabetic peripheral neuropathy (DPN). However, they do not fulfill U.S. Food and Drug Administration (FDA) requirements in relation to their capacity to assess therapeutic benefit in clinical trials of DPN. Over the past several decades symptoms and signs, quantitative sensory and electrodiagnostic testing have been strongly endorsed, but have consistently failed as surrogate end points in clinical trials. Therefore, there is an unmet need for reliable biomarkers to capture the onset and progression and to facilitate drug discovery in DPN. Corneal confocal microscopy (CCM) is a non-invasive ophthalmic imaging modality for in vivo evaluation of sensory C-fibers. An increasing body of evidence from multiple centers worldwide suggests that CCM fulfills the FDA criteria as a surrogate endpoint of DPN.

Adaptive optics optical coherence tomography in glaucoma.

Since the introduction of commercial optical coherence tomography (OCT) systems, the ophthalmic imaging modality has rapidly expanded and it has since changed the paradigm of visualization of the retina and revolutionized the management and diagnosis of neuro-retinal diseases, including glaucoma. OCT remains a dynamic and evolving imaging modality, growing from time-domain OCT to the improved spectral-domain OCT, adapting novel image analysis and processing methods, and onto the newer swept-source OCT and the implementation of adaptive optics (AO) into OCT. The incorporation of AO into ophthalmic imaging modalities has enhanced OCT by improving image resolution and quality, particularly in the posterior segment of the eye. Although OCT previously captured in-vivo cross-sectional images with unparalleled high resolution in the axial direction, monochromatic aberrations of the eye limit transverse or lateral resolution to about 15-20μm and reduce overall image quality. In pairing AO technology with OCT, it is now possible to obtain diffraction-limited resolution images of the optic nerve head and retina in three-dimensions, increasing resolution down to a theoretical 3μm3. It is now possible to visualize discrete structures within the posterior eye, such as photoreceptors, retinal nerve fiber layer bundles, the lamina cribrosa, and other structures relevant to glaucoma. Despite its limitations and barriers to widespread commercialization, the expanding role of AO in OCT is propelling this technology into clinical trials and onto becoming an invaluable modality in the clinician's arsenal.

Novel morphological macular findings in juvenile CLN3 disease

AimsJuvenile CLN3 disease, one of the most common forms of a group of lysosomal storage diseases called neuronal ceroid lipofuscinoses (NCLs), is a progressive neurodegenerative disorder with initial visual deterioration....

Development of an ophthalmological clinical information system for inpatient eye clinics

In times of increased digitalization in healthcare, departments of ophthalmology are faced with the challenge of introducing electronic clinical health records (EHR); however, specialized software for ophthalmology is not available with most major EHR sytems. The aim of this project was to create specific ophthalmological user interfaces for large inpatient eye care providers within a hospitalwide EHR. Additionally the integration of ophthalmic imaging systems, scheduling and surgical documentation should be achieved. The existing EHR i.s.h.med (Siemens, Germany) was modified using advanced business application programming (ABAP) language to create specific ophthalmological user interfaces for reproduction and moreover optimization of the clinical workflow. A user interface for documentation of ambulatory patients with eight tabs was designed. From June 2013 to October 2014 a total of 61,551 patient contact details were documented. For surgical documentation a separate user interface was set up. Digital clinical orders for documentation of registration and scheduling of operations user interfaces were also set up. A direct integration of ophthalmic imaging modalities could be established. An ophthalmologist-orientated EHR for outpatient and surgical documentation for inpatient clinics was created and successfully implemented. By incorporation of imaging procedures the foundation of future smart/big data analyses was created.

Extraction of ultra-high frequency retinal motions with a line scanning quasi-confocal ophthalmoscope

Ultra-high frequency motions of the retina severely affect the stabilization of images and result in significant imaging distortions. In this paper, a high speed line scanning quasi-confocal ophthalmoscope (LSO) capable of 160 frames per second was devised for generating stable undistorted retinal images. This technique resulted in minimal intra-frame motions, and a strip-based cross correlation algorithm with sub-pixel resolution was applied to extract retinal motions. Three retinal motion components at rates of up to 1600 Hz were clearly distinguished and extracted accurately for the first time using ophthalmic imaging methods. This was especially apparent for the fastest tremor and microsaccade movements that cannot be estimated from previously reported ophthalmic imaging instruments. Furthermore, these results were consistent with retinal motion characteristics obtained with optical lever methods, validating this technique. Actually, the LSO system has great potential to extract retinal motions, and some other tracking systems may be adopted to correct retinal motions in ophthalmic imaging modalities.

Ophthalmic imaging.

The last two decades have seen a revolution in ophthalmic imaging. In this review we present an overview of the breadth of ophthalmic imaging modalities in use today and describe how the role of ophthalmic imaging has changed from documenting abnormalities visible on clinical examination to the detection of clinically silent abnormalities which can lead to an earlier and more precise diagnosis. This review is based on published literature in the fields of ophthalmic imaging and with focus on most commonly used imaging modalities. New imaging techniques enable non-invasive evaluation of ocular structures at a resolution of a few micrometres. This has led to a re-evaluation of diagnostic criteria for ocular disease, which were previously defined by clinical findings without significant reference to imaging. Lack of formal training and clinical guidelines regarding use of new imaging techniques in diagnosing and monitoring various ocular conditions. Lack of large normative databases and interchangeability issues between different commercial machines can hinder the detection of disease progression. Imaging devices are being constantly refined with improved image capture and image analysis tools. Clinical applications of new techniques and devices have yet to be determined using systematic scientific research methods.