Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers

AIM

To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend.

METHODS

On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms.

RESULTS

Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification.

CONCLUSION

The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.

Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma

PURPOSE

We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective analysis of glaucoma.

METHODS

This cross-sectional study included 118 eyes of 94 open-angle glaucoma patients (male/female = 38/56, age: 56.1 ± 10.3 years). We used OCTA (OCT-HS100, Canon) and built-in software (RX software, v. 4.5) to perform NR and calculate RPC vessel area density (VAD) and skeleton vessel length density (VLD). We also examined NR's effect on reproducibility. Finally, we assessed the vascular structure (PRCs)/function relationship at different glaucoma stages with Spearman's correlation.

RESULTS

Regardless of NR, RPC parameters had excellent coefficients of variation (1.7-4.1%) in glaucoma patients and controls, and mean deviation (MD) was significantly correlated with VAD (NR: r = 0.835, p < 0.001; non-NR: r = 0.871, p < 0.001) and VLD (NR: r = 0.829, p < 0.001; non-NR: r = 0.837, p < 0.001). For mild, moderate, and advanced glaucoma, the correlation coefficients between MD and VLD were 0.366 (p = 0.028) 0.081 (p = 0.689), and 0.427 (p = 0.017) with NR and 0.405 (p = 0.014), 0.184 (p = 0.360), and 0.339 (p = 0.062) without NR, respectively.

CONCLUSION

Denoised RPC images might have the potential for a closer structural/functional relationship, in which the floor effect of retinal nerve fiber layer thickness affects measurements. Deep learning-based NR promises to improve glaucoma assessment.

Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms

Purpose

We formulated and tested ensemble learning models to classify axial length (AXL) from choroidal thickness (CT) as indicated on fovea-centered, 2D single optical coherence tomography (OCT) images.

Design

Retrospective cross-sectional study.

Participants

We analyzed 710 OCT images from 355 eyes of 188 patients. Each eye had 2 OCT images.

Methods

The CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. This study trained and validated the models to classify the AXLs eyes based on binary (AXL &lt; or &gt; 26 mm) and multiclass (AXL &lt; 22 mm, between 22 and 26 mm, and &gt; 26 mm) classifications.

Results

No features were redundant or duplicated after an analysis using Pearson’s correlation coefficient, LASSO-Pattern search algorithm, and variance inflation factors. Among the positions, CT at the nasal side had the highest correlation with AXL followed by the central area. In binary classification, our classifiers obtained high accuracy, as indicated by accuracy, recall, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under ROC curve (AUC) values of 94.37, 100, 90.91, 100, 86.67, and 95.61%, respectively. In multiclass classification, our classifiers were also highly accurate, as indicated by accuracy, weighted recall, weighted PPV, weighted NPV, weighted F1 score, and macro AUC of 88.73, 88.73, 91.21, 85.83, 87.42, and 93.42%, respectively.

Conclusions

Our binary and multiclass classifiers classify AXL well from CT, as indicated on OCT images. We demonstrated the effectiveness of the proposed classifiers and provided an assistance tool for physicians.

Development of glaucoma predictive model and risk factors assessment based on supervised models

Objectives

To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors.

Method

Data analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include data sampling, preprocessing, classification and evaluation and validation. Data sampling for providing the training dataset was performed with balanced sampling based on over-sampling and under-sampling methods. Data preprocessing steps were missing value imputation and normalization. For classification step, several machine learning models were designed for predicting glaucoma including Decision Trees (DTs), K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Random Forests (RFs), Extra Trees (ETs) and Bagging Ensemble methods. Moreover, in the classification step, a novel stacking ensemble model is designed and proposed using the superior classifiers.

Results

The data were from Shahroud Eye Cohort Study including demographic and ophthalmology data for 5190 participants aged 40-64 living in Shahroud, northeast Iran. The main variables considered in this dataset were 67 demographics, ophthalmologic, optometric, perimetry, and biometry features for 4561 people, including 4474 non-glaucoma participants and 87 glaucoma patients. Experimental results show that DTs and RFs trained based on under-sampling of the training dataset have superior performance for predicting glaucoma than the compared single classifiers and bagging ensemble methods with the average accuracy of 87.61 and 88.87, the sensitivity of 73.80 and 72.35, specificity of 87.88 and 89.10 and area under the curve (AUC) of 91.04 and 94.53, respectively. The proposed stacking ensemble has an average accuracy of 83.56, a sensitivity of 82.21, a specificity of 81.32, and an AUC of 88.54.

Conclusions

In this study, a machine learning model is proposed and developed to predict glaucoma disease among persons aged 40-64. Top predictors in this study considered features for discriminating and predicting non-glaucoma persons from glaucoma patients include the number of the visual field detect on perimetry, vertical cup to disk ratio, white to white diameter, systolic blood pressure, pupil barycenter on Y coordinate, age, and axial length.

Comparisons between retinal vessel calibers and various optic disc morphologic parameters with different optic disc appearances: The Glaucoma Stereo Analysis Study

The Glaucoma Stereo Analysis Study (GSAS) is a multicenter collaborative study of the characteristics of glaucomatous optic disc morphology using a stereo fundus camera. This study evaluated the retinal vessel calibers and correlations using GSAS fundus photographs between retinal vessels and 38 optic nerve head (ONH) morphologic parameters comprehensively. In all 240 eyes, the mean central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) were 138.4 and 216.5 μm, respectively; the CRAE correlated with age, visual field scores and 19 ONH parameters and CRVE correlated with age, intraocular pressure, visual field scores and 11 ONH parameters. Among the different optic disc appearances including focal ischemia (FI) (n = 53, 22%), generalized enlargement (GE) (n = 53, 22%), myopic glaucoma (MY) (n = 112, 47%), and senile sclerosis (SS) (n = 22, 9%), the CRAE did not differ significantly; CRVE was significantly narrower in SS than in FI and MY. In FI, GE, MY, and SS disc types, CRAE correlated with 3, 14, 9, and 2 ONH parameters, respectively, and CRVE corelated with 9, 0, 12, and 6 ONH parameters, respectively. We confirmed previous observations on the effect of retinal vessel narrowing on glaucomatous changes in the ONH and visual field. The associations between retinal vessel caliber and ONH morphologic parameters vary among different optic disc appearances, suggesting different effects of vascular changes in each disc type.

Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images

Deep learning is being employed in disease detection and classification based on medical images for clinical decision making. It typically requires large amounts of labelled data; however, the sample size of such medical image datasets is generally small. This study proposes a novel training framework for building deep learning models of disease detection and classification with small datasets. Our approach is based on a hierarchical classification method where the healthy/disease information from the first model is effectively utilized to build subsequent models for classifying the disease into its sub-types via a transfer learning method. To improve accuracy, multiple input datasets were used, and a stacking ensembled method was employed for final classification. To demonstrate the method’s performance, a labelled dataset extracted from volumetric ophthalmic optical coherence tomography data for 156 healthy and 798 glaucoma eyes was used, in which glaucoma eyes were further labelled into four sub-types. The average weighted accuracy and Cohen’s kappa for three randomized test datasets were 0.839 and 0.809, respectively. Our approach outperformed the flat classification method by 9.7% using smaller training datasets. The results suggest that the framework can perform accurate classification with a small number of medical images.

Artificial intelligence in ophthalmology: A new era is beginning

The use of artificial intelligence (AI) in ophthalmology is not very new and its use is expanding into various subspecialties of the eye like retina and glaucoma, thereby helping ophthalmologists to diagnose and treat diseases better than before. Incorporating “deep learning” (a subfield of AI) into image-based systems such as optical coherence tomography has dramatically improved the machine's ability to screen and identify stages of diabetic retinopathy accurately. Similar applications have been tried in the field of retinopathy of prematurity and age-related macular degeneration, a silent retinal condition that needs to be diagnosed early to prevent progression. The advent of AI into glaucoma diagnostics in analyzing visual fields and assessing disease progression also holds a promising role. The ability of the software to detect even a subtle defect that the human eye can miss has led to a revolution in the management of certain ocular conditions. However, there are few significant challenges in the AI systems, such as the incorporation of quality images, training sets and the black box dilemma. Nevertheless, despite the existing differences, there is always a chance of improving the machines/software to potentiate their efficacy and standards. This review article shall discuss the current applications of AI in ophthalmology, significant challenges and the prospects as to how both science and medicine can work together.

Optic nerve head anatomy in myopia and glaucoma, including parapapillary zones alpha, beta, gamma and delta: Histology and clinical features

The optic nerve head can morphologically be differentiated into the optic disc with the lamina cribrosa as its basis, and the parapapillary region with zones alpha (irregular pigmentation due to irregularities of the retinal pigment epithelium (RPE) and peripheral location), beta zone (complete RPE loss while Bruch's membrane (BM) is present), gamma zone (absence of BM), and delta zone (elongated and thinned peripapillary scleral flange) within gamma zone and located at the peripapillary ring. Alpha zone is present in almost all eyes. Beta zone is associated with glaucoma and may develop due to a IOP rise-dependent parapapillary up-piling of RPE. Gamma zone may develop due to a shift of the non-enlarged BM opening (BMO) in moderate myopia, while in highly myopic eyes, the BMO enlarges and a circular gamma zone and delta zone develop. The ophthalmoscopic shape and size of the optic disc is markedly influenced by a myopic shift of BMO, usually into the temporal direction, leading to a BM overhanging into the intrapapillary compartment at the nasal disc border, a secondary lack of BM in the temporal parapapillary region (leading to gamma zone in non-highly myopic eyes), and an ocular optic nerve canal running obliquely from centrally posteriorly to nasally anteriorly. In highly myopic eyes (cut-off for high myopia at approximately -8 diopters or an axial length of 26.5 mm), the optic disc area enlarges, the lamina cribrosa thus enlarges in area and decreases in thickness, and the BMO increases, leading to a circular gamma zone and delta zone in highly myopic eyes.

Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL

We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch’s membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learning model. Discriminating early-stage glaucoma and GS is challenging and a deep-learning model may be helpful to clinicians. NTG accounts for an average 77% of open-angle glaucoma in Asians. BMO-MRW is a new structural parameter that has advantages in assessing neuroretinal rim tissue more accurately than conventional parameters. A dataset consisted of 229 eyes out of 277 GS and 168 eyes of 285 patients with early NTG. A deep-learning algorithm was developed to discriminate between GS and early NTG using a training set, and its accuracy was validated in the testing dataset using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The deep neural network model (DNN) achieved highest diagnostic performance, with an AUC of 0.966 (95%confidence interval 0.929–1.000) in classifying either GS or early NTG, while AUCs of 0.927–0.947 were obtained by other machine-learning models. The performance of the DNN model considering all three OCT-based parameters was the highest (AUC 0.966) compared to the combinations of just two parameters. As a single parameter, BMO-MRW (0.959) performed better than RNFL alone (0.914).

A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children

Background

Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation of AL, have been conducted. The purpose of our study was to construct a machine learning (ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children.

Methods

In total, 1011 myopic children aged 6 to 18 years participated in this study. Cross-sectional datasets were used to optimize the ML algorithms. The input variables included age, sex, central corneal thickness (CCT), spherical equivalent refractive error (SER), mean K reading (K-mean), and white-to-white corneal diameter (WTW). The output variable was AL. A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups, including 4 groups used as training data and one group used as validation data. Six types of ML algorithms were implemented in our models. The best-performing algorithm was applied to predict AL, and estimates of the physiological elongation of AL were obtained as the partial derivatives of AL_predicted-age curves based on an unchanged SER value with increasing age.

Results

Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a R² value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the AL_predicted-age curves, the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age, SER and K-mean. According to the model, the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean.

Conclusions

The physiological elongation of the AL is rarely recorded in clinical data in China. In cases of unavailable clinical data, an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL, which is especially useful when monitoring myopia progression in orthokeratology lens wearers.

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Purpose

This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression.

Methods

Nonsystematic literature review using the search combinations “Artificial Intelligence,” “Deep Learning,” “Machine Learning,” “Neural Networks,” “Bayesian Networks,” “Glaucoma Diagnosis,” and “Glaucoma Progression.” Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted.

Results

Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test.

Conclusions

AI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test. However, for patients who are likely to have glaucoma more sophisticated methods should be used including data from OCT and perimetry. Outputs may serve as an adjunct to assist clinical decision making, whereas also enhancing the efficiency, productivity, and quality of the delivery of glaucoma care. Patients with diagnosed glaucoma may benefit from future algorithms to evaluate their risk of progression. Challenges are yet to be overcome, including the external validity of AI strategies, a move from a “black box” toward “explainable AI,” and likely regulatory hurdles. However, it is clear that AI can enhance the role of specialist clinicians and will inevitably shape the future of the delivery of glaucoma care to the next generation.

Translational Relevance

The promising levels of diagnostic accuracy reported by AI strategies across the modalities used in clinical practice for glaucoma detection can pave the way for the development of reliable models appropriate for their translation into clinical practice. Future incorporation of AI into healthcare models may help address the current limitations of access and timely management of patients with glaucoma across the world.

The potential application of artificial intelligence for diagnosis and management of glaucoma in adults

BACKGROUND

Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma.

SOURCES OF DATA

This literature review is based on articles published in peer-reviewed journals.

AREAS OF AGREEMENT

There have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior.

AREAS OF CONTROVERSY

Concerns that the increased reliance on AI may lead to deskilling of clinicians.

GROWING POINTS

AI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable.

AREAS TIMELY FOR DEVELOPING RESEARCH

There is a need to determine the external validity of deep learning algorithms and to better understand how the 'black box' paradigm reaches results.

Application of machine learning in ophthalmic imaging modalities

In clinical ophthalmology, a variety of image-related diagnostic techniques have begun to offer unprecedented insights into eye diseases based on morphological datasets with millions of data points. Artificial intelligence (AI), inspired by the human multilayered neuronal system, has shown astonishing success within some visual and auditory recognition tasks. In these tasks, AI can analyze digital data in a comprehensive, rapid and non-invasive manner. Bioinformatics has become a focus particularly in the field of medical imaging, where it is driven by enhanced computing power and cloud storage, as well as utilization of novel algorithms and generation of data in massive quantities. Machine learning (ML) is an important branch in the field of AI. The overall potential of ML to automatically pinpoint, identify and grade pathological features in ocular diseases will empower ophthalmologists to provide high-quality diagnosis and facilitate personalized health care in the near future. This review offers perspectives on the origin, development, and applications of ML technology, particularly regarding its applications in ophthalmic imaging modalities.

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard

PURPOSE

To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.

DESIGN

Retrospective, cross-sectional, longitudinal cohort study.

PARTICIPANTS

Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models.

METHOD

We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma.

MAIN OUTCOME MEASURES

The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma.

RESULTS

After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%.

CONCLUSIONS

The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression.

Examination of retinal vascular trajectory in schizophrenia and bipolar disorder

AIM

Evidence suggests microvascular dysfunction (wider retinal venules and narrower arterioles) in schizophrenia (SCZ) and bipolar disorder (BD). The vascular development is synchronous with neuronal development in the retina and brain. The retinal vessel trajectory is related to retinal nerve fiber layer thinning and cerebrovascular abnormalities in SCZ and BD and has not yet been examined. Hence, in this study we examined the retinal vascular trajectory in SCZ and BD in comparison with healthy volunteers (HV).

METHODS

Retinal images were acquired from 100 HV, SCZ patients, and BD patients, respectively, with a non-mydriatic fundus camera. Images were quantified to obtain the retinal arterial and venous trajectories using a validated, semiautomated algorithm. Analysis of covariance and regression analyses were conducted to examine group differences. A supervised machine-learning ensemble of bagged-trees method was used for automated classification of trajectory values.

RESULTS

There was a significant difference among groups in both the retinal venous trajectory (HV: 0.17 ± 0.08; SCZ: 0.25 ± 0.17; BD: 0.27 ± 0.20; P < 0.001) and the arterial trajectory (HV: 0.34 ± 0.15; SCZ: 0.29 ± 0.10; BD: 0.29 ± 0.11; P = 0.003) even after adjusting for age and sex (P < 0.001). On post-hoc analysis, the SCZ and BD groups differed from the HV on retinal venous and arterial trajectories, but there was no difference between SCZ and BD patients. The machine learning showed an accuracy of 86% and 73% for classifying HV versus SCZ and BD, respectively.

CONCLUSION

Smaller trajectories of retinal arteries indicate wider and flatter curves in SCZ and BD. Considering the relation between retinal/cerebral vasculatures and retinal nerve fiber layer thinness, the retinal vascular trajectory is a potential marker for SCZ and BD. As a relatively affordable investigation, retinal fundus photography should be further explored in SCZ and BD as a potential screening measure.

Joint retina segmentation and classification for early glaucoma diagnosis

We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.

[Can Big Data change our practices?]

The European Medicines Agency has defined Big Data by the "3 V's": Volume, Velocity and Variety. These large databases allow access to real life data on patient care. They are particularly suited for studies of adverse events and pharmacoepidemiology. Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data using model architectures, which are composed of multiple nonlinear transformations. This article shows how Big Data and Deep Learning can help in ophthalmology, pointing out their advantages and disadvantages. A literature review is presented in this article illustrating the uses of Deep Learning in ophthalmology.

Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics

OBJECTIVES

To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitary disease from among a glaucomatous population.

METHODS

24-2 Humphrey Visual Field reports were gathered from 121 pituitary patients and 907 glaucomatous patients. Optical character recognition was used to extract the threshold values from PDF reports. Left and right eye visual fields were coupled for each patient in an array to create bilateral field representations. ANNs were created to detect chiasmal field defects. We also assessed the ability of ANNs to identify a single pituitary field among 907 glaucomatous distractors.

RESULTS

Mean field thresholds across all locations were lower for pituitary patients (20.3 dB, SD = 5.2 dB) than for glaucoma patients (24.4 dB, SD = 5.0 dB) indicating a greater degree of field loss (p < 0.0001) in the pituitary group. However, substantial overlap between the groups meant that mean bilateral field loss was not a reliable indicator of aetiology. Representative ANNs showed good performance in the discrimination task with sensitivity and specificity routinely above 95%. Where a single pituitary field was hidden among 907 glaucomatous fields, it had one of the five highest indexes of suspicion on 91% of 2420 ANNs.

CONCLUSIONS

Traditional artificial neural networks perform well at detecting chiasmal field defects among a glaucoma cohort by inspecting bilateral field representations. Increasing automation of care means we will need robust methods of automatically diagnosing and managing disease. This work shows that machine learning can perform a useful role in diagnostic oversight in highly automated glaucoma clinics, enhancing patient safety.

Validation of formula-predicted glaucomatous optic disc appearances: the Glaucoma Stereo Analysis Study

AIMS

The Glaucoma Stereo Analysis Study (GSAS) is a multicentre collaborative study of the characteristics of glaucomatous optic disc morphology using a stereo fundus camera. Using the GSAS dataset, we previously established a formula for predicting different appearances of glaucomatous optic discs, although the formula lacked validation in an independent dataset. In this study, the formula was validated in another testing dataset.

SUBJECTS AND METHODS

Testing dataset contained three-dimensionally analysed optic disc topographic parameters from 93 eyes with primary open-angle glaucoma; six topographic parameters (temporal and nasal rim-disc ratios, mean cup depth, height variation contour, disc tilt angle and rim decentring absolute value) were used for predicting different appearances of glaucomatous optic discs. The agreement between grader-classified optic disc types, that is, focal ischemic (FI), generalized enlargement, myopic glaucomatous (MY), and senile sclerotic (SS) and formula-predicted optic disc types, that is, pFI, pGE, pMY and pSS, were assessed.

RESULTS

Based on this formula, the eyes were classified with pFI (21 eyes, 22.6%), pGE (27 eyes, 29.0%), pMY (26 eyes, 28.0%) and pSS (19 eyes, 20.4%) when the top predictive element based on the formula was considered as the optic disc appearance in each eye. The six topographic parameters used in the formula differed significantly among the four predicted optic disc types. Substantial agreement (κ = 0.7496) was seen for the top two predictive elements based on the formula that agreed with the graders' classification in 76 (81.7%) eyes. Among the four optic disc types, the levels of agreement were relatively lower in the SS type (κ = 0.3863-0.5729) compared with the other three optic disc types (κ = 0.7898-0.8956) even though the unclassifiable and mixed optic disc types were excluded from the testing dataset.

CONCLUSION

The GSAS classification formula can predict and quantify each component of different optic disc appearances in each eye and provide a novel parameter to describe glaucomatous optic disc characteristics.

Application of artificial intelligence in ophthalmology

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.

Comparison of Machine-Learning Classification Models for Glaucoma Management

This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients' background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.