Applications of deep learning in detection of glaucoma: A systematic review

Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis

Purpose: To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. Materials and Methods: Relevant studies published up to 30 October 2024 were retrieved from PubMed, Medline, EMBASE, Cochrane Library, Web of Science, and ClinicalKey. A bivariate random-effects model was employed to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, and area under the receiver operating characteristic curve (AUROC). Results: A total of 48 studies were included in the meta-analysis. DL algorithms demonstrated high diagnostic performance in glaucoma detection using fundus photography and OCT images. For fundus photography, the pooled sensitivity and specificity were 0.92 (95% CI: 0.89-0.94) and 0.93 (95% CI: 0.90-0.95), respectively, with an AUROC of 0.90 (95% CI: 0.88-0.92). For the OCT imaging, the pooled sensitivity and specificity were 0.90 (95% CI: 0.84-0.94) and 0.87 (95% CI: 0.81-0.91), respectively, with an AUROC of 0.86 (95% CI: 0.83-0.90). In predicting glaucoma progression, DL models generally showed less robust performance, with pooled sensitivities and specificities ranging lower than in diagnostic tasks. Internal validation datasets showed higher accuracy than external validation datasets. Conclusions: DL algorithms achieve excellent performance in diagnosing glaucoma using fundus photography and OCT imaging. To enhance the prediction of glaucoma progression, future DL models should integrate multimodal data, including functional assessments, such as visual field measurements, and undergo extensive validation in real-world clinical settings.

Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis

BACKGROUND

In recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. Some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility.

OBJECTIVE

This study aims to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice, thereby providing evidence-based support for the future development and refinement of intelligent diagnostic or predictive tools.

METHODS

PubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The prediction model risk of bias assessment tool was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented using a bivariate mixed-effects model. In the validation set, subgroup analyses were conducted based on the ML target events (diagnosis and prediction of high myopia and diagnosis of pathological myopia and high myopia-associated glaucoma) and modeling methods.

RESULTS

This study ultimately included 45 studies, of which 32 were used for quantitative meta-analysis. The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI 0.95-0.98), 0.91 (95% CI 0.89-0.92), and 0.95 (95% CI 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI 0.95-0.98), sensitivity of 0.92 (95% CI 0.90-0.93), and specificity of 0.96 (95% CI 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI 0.75-0.92), sensitivity of 0.77 (95% CI 0.69-0.84), and specificity of 0.85 (95% CI 0.75-0.92). For the diagnosis and prediction of high myopia, the SROC, sensitivity, and specificity of ML were 0.98 (95% CI 0.96-0.99), 0.94 (95% CI 0.90-0.96), and 0.94 (95% CI 0.88-0.97), respectively. For the diagnosis of high myopia-associated glaucoma, the SROC, sensitivity, and specificity of ML were 0.96 (95% CI 0.94-0.97), 0.92 (95% CI 0.85-0.96), and 0.88 (95% CI 0.67-0.96), respectively.

CONCLUSIONS

ML demonstrated highly promising accuracy in diagnosing high myopia and pathologic myopia. Moreover, based on the limited evidence available, we also found that ML appeared to have favorable accuracy in predicting the risk of developing high myopia in the future. DL can be used as a potential method for intelligent image processing and intelligent recognition, and intelligent examination tools can be developed in subsequent research to provide help for areas where medical resources are scarce.

TRIAL REGISTRATION

PROSPERO CRD42023470820; https://tinyurl.com/2xexp738.

A preliminary study of artificial intelligence to recognize tessellated fundus in visual function screening of 7-14 year old students

BACKGROUND

To evaluate the accuracy of artificial intelligence (AI)-based technology in recognizing tessellated fundus in students aged 7-14 years.

METHODS

A retrospective study was conducted to collect consecutive fundus photographs for visual function screening of students aged 7-14 years old in Haikou City from June 2018 to May 2019, and 1907 cases were included in the study. Among them, 949 cases were male and 958cases were female. The results were manually analyzed by two attending ophthalmologists to ensure the accuracy of the results. In case of discrepancies between the results analyzed by the two methods, the manual results were used as the standard. To assess the sensitivity and specificity of AI in recognizing tessellated fundus, a Kappa consistency test was performed comparing the results of manual recognition with those of AI recognition.

RESULTS

Among 1907 cases, 1782 cases, or 93.4%, were completely consistent with the recognition results of manual and AI; 125 cases, or 6.6%, were analyzed with differences. The diagnostic rates of manual and AI for tessellated fundus were 26.1% and 26.4%, respectively. The sensitivity, specificity and area of the ROC curve (AUC) of AI for recognizing tessellated fundus in students aged 7-14 years were 88.0%, 95.4% and 0.917, respectively. The results of test showed that that the manual and AI identification results were highly consistent (κ = 0.831, P = 0.000).

CONCLUSION

AI analysis has high specificity and sensitivity for tessellated fundus identification in students aged 7-14 years, and it is feasible to apply artificial intelligence to visual function screening in students aged 7-14 years.

Artificial Intelligence and Advanced Technology in Glaucoma: A Review

BACKGROUND

Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions.

AIM

This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions.

METHODS

A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included.

RESULTS

The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care.

CONCLUSIONS

Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.

Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis

PRCIS

In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively.

PURPOSE

This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle closure in patients with glaucoma.

METHODS

We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity.

RESULTS

The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI: 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI: 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1%-95.1%. Similarly, specificity results ranged from 90.3% to 94.5% with the removal of each individual study and recalculation of pooled specificity.

CONCLUSION

DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.

The use of artificial neural networks in studying the progression of glaucoma

In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilities and support physicians in diagnosing and treating patients. This paper focuses on modelling glaucoma evolution because it requires early diagnosis, individualized treatment, and lifelong monitoring. Glaucoma is a chronic, progressive, irreversible, multifactorial optic neuropathy that primarily affects elderly individuals. It is important to emphasize that the processed data are taken from medical records, unlike other studies in the literature that rely on image acquisition and processing. Although more challenging to handle, this approach has the advantage of including a wide range of parameters in large numbers, which can highlight their potential influence. Artificial neural networks are used to study glaucoma progression, designed through successive trials for near-optimal configurations using the NeuroSolutions and PyTorch frameworks. Furthermore, different problems are formulated to demonstrate the influence of various structural and functional parameters on the study of glaucoma progression. Optimal neural networks were obtained using a program written in Python using the PyTorch deep learning framework. For various tasks, very small errors in training and validation, under 5%, were obtained. It has been demonstrated that very good results can be achieved, making them credible and useful for medical practice.

Telemedicine for Screening and Follow-Up of Glaucoma: A Descriptive Study

Introduction: Glaucoma is a leading cause of irreversible blindness. It is a prevalent disease worldwide, affecting ∼70 million people and expected to reach up to 112 million by 2040. Purpose: The aim of this study is to describe the implementation and initial experience of a telemedicine program to monitor glaucoma and glaucoma suspect patients in a large, integrated health care system during the COVID-19 pandemic. Methods: A retrospective chart review of established glaucoma or glaucoma suspect patients who participated in a telemedicine evaluation at the ophthalmic center of a large, Colombian health care system between June 2020 and April 2023 was conducted. Clinical and sociodemographic variables were analyzed. Generated clinical orders for additional testing, surgical procedures, follow-ups, and referrals, as well as changes in medical treatment, were evaluated. Results: A total of 11,034 telemedicine consults were included. The mean ± standard deviation age of this group was 63 ± 17.2 years and 67% were female. Of the patients who attended teleconsults, 49% were glaucoma suspects and 38.5% were followed with a diagnosis of open-angle glaucoma. After the consult, 25% of patients were referred to a glaucoma specialist, 40% had additional testing ordered, and 8% had a surgical procedure ordered, mainly laser iridotomy (409 cases). Almost a third of patients returned for subsequent telemedicine visits after the initial encounter. Despite some technical difficulties, 99.8% of patients attended and completed their scheduled telemedicine appointments. Conclusions: A telemedicine program aimed to monitor established glaucoma patients can be successfully implemented. Established patients within an integrated health care system have high adherence to the virtual model. Further research by health care institutions and government agencies will be key to expand coverage to additional populations. Clinical Trial Registration Number: CEIFUS 1026-24.

Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images

Aim

Conventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images.

Methods

This research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology.

Results

The ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%.

Conclusion

Among various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.

RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps

Optical coherence tomography imaging provides a crucial clinical measurement for diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer (RNFL) thickness (RNFLT) map. Researchers have been increasingly using neural models to extract meaningful features from the RNFLT map, aiming to identify biomarkers for glaucoma and its progression. However, accurately representing the RNFLT map features relevant to glaucoma is challenging due to significant variations in retinal anatomy among individuals, which confound the pathological thinning of the RNFL. Moreover, the presence of artifacts in the RNFLT map, caused by segmentation errors in the context of degraded image quality and defective imaging procedures, further complicates the task. In this paper, we propose a general framework called RNFLT2Vec for unsupervised learning of vectorized feature representations from RNFLT maps. Our method includes an artifact correction component that learns to rectify RNFLT values at artifact locations, producing a representation reflecting the RNFLT map without artifacts. Additionally, we incorporate two regularization techniques to encourage discriminative representation learning. Firstly, we introduce a contrastive learning-based regularization to capture the similarities and dissimilarities between RNFLT maps. Secondly, we employ a consistency learning-based regularization to align pairwise distances of RNFLT maps with their corresponding thickness distributions. Through extensive experiments on a large-scale real-world dataset, we demonstrate the superiority of RNFLT2Vec in three different clinical tasks: RNFLT pattern discovery, glaucoma detection, and visual field prediction. Our results validate the effectiveness of our framework and its potential to contribute to a better understanding and diagnosis of glaucoma.

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

Insights into artificial intelligence in myopia management: from a data perspective

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma

Ophthalmic images, along with their derivatives like retinal nerve fiber layer (RNFL) thickness maps, play a crucial role in detecting and monitoring eye diseases such as glaucoma. For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e.g., RNFL thinning patterns) associated with functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. This challenge is further amplified by the presence of image artifacts, commonly resulting from image acquisition and automated segmentation issues. In this paper, we present an artifact-tolerant unsupervised learning framework called EyeLearn for learning ophthalmic image representations in glaucoma cases. EyeLearn includes an artifact correction module to learn representations that optimally predict artifact-free images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the affinities within and between images. During training, images are dynamically organized into clusters to form contrastive samples, which encourage learning similar or dissimilar representations for images in the same or different clusters, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection with a real-world dataset of glaucoma patient ophthalmic images. Extensive experiments and comparisons with state-of-the-art methods confirm the effectiveness of EyeLearn in learning optimal feature representations from ophthalmic images.

Relative Importance of Glaucoma-Referral Indicators in Retinal Images in a Diabetic Retinopathy Screening Programme in Slovenia: A Cross-Sectional Study

Background and Objectives: Glaucoma is a major cause of irreversible visual impairment and blindness, so its timely detection is crucial. Retinal images from diabetic retinopathy screening programmes (DRSP) provide an opportunity to detect undiagnosed glaucoma. Our aim was to find out which retinal image indicators are most suitable for referring DRSP patients for glaucoma assessment and to determine the glaucoma detection potential of Slovenian DRSP. Materials and Methods: We reviewed retinal images of patients from the DRSP at the University Medical Centre Ljubljana (November 2019-January 2020, May-August 2020). Patients with at least one indicator and some randomly selected patients without indicators were invited for an eye examination. Suspect glaucoma and glaucoma patients were considered accurately referred. Logistic regression (LOGIT) with patients as statistical units and generalised estimating equation with logistic regression (GEE) with eyes as statistical units were used to determine the referral accuracy of indicators. Results: Of the 2230 patients reviewed, 209 patients (10.1%) had at least one indicator on a retinal image of either one eye or both eyes. A total of 149 (129 with at least one indicator and 20 without) attended the eye exam. Seventy-nine (53.0%) were glaucoma negative, 54 (36.2%) suspect glaucoma, and 16 (10.7%) glaucoma positive. Seven glaucoma patients were newly detected. Neuroretinal rim notch predicted glaucoma in all cases. The cup-to-disc ratio was the most important indicator for accurate referral (odds ratio 7.59 (95% CI 3.98-14.47; p < 0.001) and remained statistically significant multivariably. Family history of glaucoma also showed an impact (odds ratio 3.06 (95% CI 1.02-9.19; p = 0.046) but remained statistically significant only in the LOGIT multivariable model. Other indicators and confounders were not statistically significant in the multivariable models. Conclusions: Our results suggest that the neuroretinal rim notch and cup-to-disc ratio are the most important for accurate glaucoma referral from retinal images in DRSP. Approximately half of the glaucoma cases in DRSPs may be undiagnosed.

A machine learning framework for the quantification of experimental uveitis in murine OCT

This paper presents methods for the detection and assessment of non-infectious uveitis, a leading cause of vision loss in working age adults. In the first part, we propose a classification model that can accurately predict the presence of uveitis and differentiate between different stages of the disease using optical coherence tomography (OCT) images. We utilize the Grad-CAM visualization technique to elucidate the decision-making process of the classifier and gain deeper insights into the results obtained. In the second part, we apply and compare three methods for the detection of detached particles in the retina that are indicative of uveitis. The first is a fully supervised detection method, the second is a marked point process (MPP) technique, and the third is a weakly supervised segmentation that produces per-pixel masks as output. The segmentation model is used as a backbone for a fully automated pipeline that can segment small particles of uveitis in two-dimensional (2-D) slices of the retina, reconstruct the volume, and produce centroids as points distribution in space. The number of particles in retinas is used to grade the disease, and point process analysis on centroids in three-dimensional (3-D) shows clustering patterns in the distribution of the particles on the retina.

Automatic medical specialty classification based on patients' description of their symptoms

In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models.

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma an examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is noninvasive and low-cost; however, image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?" Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011 to 2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modeling-based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research.

Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets

CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the small sample size of many medical data sets dampens performance and results in overfitting. In some medical areas, it is simply too labor-intensive and expensive to amass images numbering in the hundreds of thousands. Building Deep CNN ensembles of pre-trained CNNs is one powerful method for overcoming this problem. Ensembles combine the outputs of multiple classifiers to improve performance. This method relies on the introduction of diversity, which can be introduced on many levels in the classification workflow. A recent ensembling method that has shown promise is to vary the activation functions in a set of CNNs or within different layers of a single CNN. This study aims to examine the performance of both methods using a large set of twenty activations functions, six of which are presented here for the first time: 2D Mexican ReLU, TanELU, MeLU + GaLU, Symmetric MeLU, Symmetric GaLU, and Flexible MeLU. The proposed method was tested on fifteen medical data sets representing various classification tasks. The best performing ensemble combined two well-known CNNs (VGG16 and ResNet50) whose standard ReLU activation layers were randomly replaced with another. Results demonstrate the superiority in performance of this approach.

Real-Time Risk Score for Glaucoma Mass Screening by Spectral Domain Optical Coherence Tomography: Development and Validation

Purpose

To develop and validate a risk score assessable in real-time using only retinal thickness-related values measured by spectral domain optical coherence tomography alone for use in population-based glaucoma mass screenings.

Methods

A total of 7572 participants (aged 35-74 years) underwent spectral domain optical coherence tomography examination annually between 2016 to 2021 in a population-based setting. We selected 284 glaucoma cases and 284 controls, matched by age and sex, from 11,487 scans in 2016. We conducted multivariable logistic regression with backward stepwise selection of retinal thickness-related variables to develop the diagnostic models. The developed risk scores were applied to all participants in 2018 (9720 eyes), and we randomly selected 723 scans for validation. Additional validation using the Humphrey field analyzer was conducted on 129 eyes in 2020. We assessed the models using sensitivity, specificity, the area under the receiver operating characteristic curve and positive and negative predictive values.

Results

The best-predicting model achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.96-0.98) with a sensitivity of 0.93 and specificity of 0.91. The validation dataset showed a positive predictive value of 90.8% for high-risk scorers, corresponding to 6.2% of the population, and negative predictive value of 88.2% for low-risk scorers, corresponding to 85.2%. Sensitivity and specificity for glaucoma diagnosis were 0.85 and 0.91, when we set the risk score cut-off at 90 points out of 100.

Conclusions

This risk score could be used as a valid index for glaucoma screening in a population-based setting.

Translational Relevance

The score is feasible by installing a simple computer application on an existing spectral domain optical coherence tomography and will help to improve the accuracy and efficiency of glaucoma screening.

A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning

Glaucoma is an optic neuropathy that leads to characteristic visual field defects. However, there is no cure for glaucoma, so the diagnosis of its severity is essential for its prevention. In this paper, we propose a multimodal classification architecture based on deep learning for the severity diagnosis of glaucoma. In this architecture, a gray scale image of the visual field is first reconstructed with a higher resolution in the preprocessing stage, and more subtle feature information is provided for glaucoma diagnosis. We then use multimodal fusion technology to integrate fundus images and gray scale images of the visual field as the input of this architecture. Finally, the inherent limitation of convolutional neural networks (CNNs) is addressed by replacing the original classifier with the proposed classifier. Our architecture is trained and tested on the datasets provided by the First Affiliated Hospital of Kunming Medical University, and the results show that the proposed architecture achieves superior performance for glaucoma diagnosis.

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.

Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models' performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists' (experts) precious time and involvement.

Pathologic myopia: advances in imaging and the potential role of artificial intelligence

Pathologic myopia is a severe form of myopia that can lead to permanent visual impairment. The recent global increase in the prevalence of myopia has been projected to lead to a higher incidence of pathologic myopia in the future. Thus, imaging myopic eyes to detect early pathological changes, or predict myopia progression to allow for early intervention, has become a key priority. Recent advances in optical coherence tomography (OCT) have contributed to the new grading system for myopic maculopathy and myopic traction maculopathy, which may improve phenotyping and thus, clinical management. Widefield fundus and OCT imaging has improved the detection of posterior staphyloma. Non-invasive OCT angiography has enabled depth-resolved imaging for myopic choroidal neovascularisation. Artificial intelligence (AI) has shown great performance in detecting pathologic myopia and the identification of myopia-associated complications. These advances in imaging with adjunctive AI analysis may lead to improvements in monitoring disease progression or guiding treatments. In this review, we provide an update on the classification of pathologic myopia, how imaging has improved clinical evaluation and management of myopia-associated complications, and the recent development of AI algorithms to aid the detection and classification of pathologic myopia.

Normal-tension glaucoma: Current concepts and approaches-A review

Normal tension glaucoma (NTG) has remained a challenging disease. We review, from an epidemiological perspective, why we should redefine normality, act earlier at lower pre-treatment intraocular pressure (IOP) level, and the role of ocular perfusion pressures, noting that perfusion is affected by defective vascular bed autoregulation and endothelial dysfunction. The correlation of silent cerebral infarcts (SCI) and NTG may indicate that NTG belongs to a wider spectrum of small vessel diseases (SVD), with its main pathology being also on vascular endothelium. Epidemiological studies also suggested that vascular geometry, such as fractal dimension, may affect perfusion efficiency, occurrence of SCI, SVD and glaucoma. Artificial intelligence with deep learning, may help predicting NTG progression from vascular geometry. Finally, we review latest evidence on the role of minimally-invasive glaucoma surgery, lasers, and newer drugs. We conclude that IOP is not the only modifiable risk factors as, many vascular risk factors are readily modifiable.

Comparison of retinal nerve fiber layer, macular ganglion cell complex and choroidal thickness in patients with migraine with and without aura by using optical coherence tomography

BACKGROUND

We compared the choroidal thickness (CT), peripapillary retinal nerve fibre layer thickness (pRNFLT) and macular ganglion cell complex thickness (mGCCT) by using spectral domain optic coherence tomography (SD-OCT) in patients with migraine with aura (MWA), migraine without aura (MWoA), and healthy controls.

METHODS

Thirty-seven patients with MWA, 40 patients with MWoA, and age and sex-matched 50 healthy controls were included in this cross-sectional study. CTs at fovea, nasal to fovea and temporal to fovea, global pRNFLT, four quadrants of pRNFLTs, mGCCTs in superior and inferior hemisphere were measured by SD-OCT. The duration of migraine, monthly attack number and the migraine disability assessment (MIDAS) questionnaire scores were recorded.

RESULTS

The mean foveal CT, nasal CT, and temporal CT in patients with MWA were significantly thinner than those of patients with MWoA and control (p < 0.001) while CTs of patients with MWoA were similar with those of controls. Patients with MWA and MWoA had thinner global pRNFLT, superior and inferior pRNFLT compared to controls but there were no significant differences between two migraineurs groups. Only nasal quadrant of pRNFLT was significantly thinner in patients with MWA than other groups. The superior and inferior mGCCTs were significantly thinner in patients with MWA and MWoA than controls.

CONCLUSION

Our results suggested that dysregulation of blood flow in ocular tissues caused by impairment of autoregulation in migraine. Patients with MWA might have an additional risk of choroidal and retinal ischemia than patients with MWoA and healthy controls.