Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images

Wavelet-based selection-and-recalibration network for Parkinson's disease screening in OCT images

BACKGROUND AND OBJECTIVE

Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD screening is crucial for early clinical intervention and treatment. Recent clinical research indicates that changes in pathology, such as the texture and thickness of the retinal layers, can serve as biomarkers for clinical PD diagnosis based on optical coherence tomography (OCT) images. However, the pathological manifestations of PD in the retinal layers are subtle compared to the more salient lesions associated with retinal diseases.

METHODS

Inspired by textural edge feature extraction in frequency domain learning, we aim to explore a potential approach to enhance the distinction between the feature distributions in retinal layers of PD cases and healthy controls. In this paper, we introduce a simple yet novel wavelet-based selection and recalibration module to effectively enhance the feature representations of the deep neural network by aggregating the unique clinical properties, such as the retinal layers in each frequency band. We combine this module with the residual block to form a deep network named Wavelet-based Selection and Recalibration Network (WaveSRNet) for automatic PD screening.

RESULTS

The extensive experiments on a clinical PD-OCT dataset and two publicly available datasets demonstrate that our approach outperforms state-of-the-art methods. Visualization analysis and ablation studies are conducted to enhance the explainability of WaveSRNet in the decision-making process.

CONCLUSIONS

Our results suggest the potential role of the retina as an assessment tool for PD. Visual analysis shows that PD-related elements include not only certain retinal layers but also the location of the fovea in OCT images.

Artificial intelligence for diagnosing exudative age-related macular degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a retinal disorder characterized by central retinal (macular) damage. Approximately 10% to 20% of non-exudative AMD cases progress to the exudative form, which may result in rapid deterioration of central vision. Individuals with exudative AMD (eAMD) need prompt consultation with retinal specialists to minimize the risk and extent of vision loss. Traditional methods of diagnosing ophthalmic disease rely on clinical evaluation and multiple imaging techniques, which can be resource-consuming. Tests leveraging artificial intelligence (AI) hold the promise of automatically identifying and categorizing pathological features, enabling the timely diagnosis and treatment of eAMD.

OBJECTIVES

To determine the diagnostic accuracy of artificial intelligence (AI) as a triaging tool for exudative age-related macular degeneration (eAMD).

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, three clinical trials registries, and Data Archiving and Networked Services (DANS) for gray literature. We did not restrict searches by language or publication date. The date of the last search was April 2024.

SELECTION CRITERIA

Included studies compared the test performance of algorithms with that of human readers to detect eAMD on retinal images collected from people with AMD who were evaluated at eye clinics in community or academic medical centers, and who were not receiving treatment for eAMD when the images were taken. We included algorithms that were either internally or externally validated or both.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently extracted data and assessed study quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool with revised signaling questions. For studies that reported more than one set of performance results, we extracted only one set of diagnostic accuracy data per study based on the last development stage or the optimal algorithm as indicated by the study authors. For two-class algorithms, we collected data from the 2x2 table whenever feasible. For multi-class algorithms, we first consolidated data from all classes other than eAMD before constructing the corresponding 2x2 tables. Assuming a common positivity threshold applied by the included studies, we chose random-effects, bivariate logistic models to estimate summary sensitivity and specificity as the primary performance metrics.

MAIN RESULTS

We identified 36 eligible studies that reported 40 sets of algorithm performance data, encompassing over 16,000 participants and 62,000 images. We included 28 studies (78%) that reported 31 algorithms with performance data in the meta-analysis. The remaining nine studies (25%) reported eight algorithms that lacked usable performance data; we reported them in the qualitative synthesis. Study characteristics and risk of bias Most studies were conducted in Asia, followed by Europe, the USA, and collaborative efforts spanning multiple countries. Most studies identified study participants from the hospital setting, while others used retinal images from public repositories; a few studies did not specify image sources. Based on four of the 36 studies reporting demographic information, the age of the study participants ranged from 62 to 82 years. The included algorithms used various retinal image types as model input, such as optical coherence tomography (OCT) images (N = 15), fundus images (N = 6), and multi-modal imaging (N = 7). The predominant core method used was deep neural networks. All studies that reported externally validated algorithms were at high risk of bias mainly due to potential selection bias from either a two-gate design or the inappropriate exclusion of potentially eligible retinal images (or participants). Findings Only three of the 40 included algorithms were externally validated (7.5%, 3/40). The summary sensitivity and specificity were 0.94 (95% confidence interval (CI) 0.90 to 0.97) and 0.99 (95% CI 0.76 to 1.00), respectively, when compared to human graders (3 studies; 27,872 images; low-certainty evidence). The prevalence of images with eAMD ranged from 0.3% to 49%. Twenty-eight algorithms were reportedly either internally validated (20%, 8/40) or tested on a development set (50%, 20/40); the pooled sensitivity and specificity were 0.93 (95% CI 0.89 to 0.96) and 0.96 (95% CI 0.94 to 0.98), respectively, when compared to human graders (28 studies; 33,409 images; low-certainty evidence). We did not identify significant sources of heterogeneity among these 28 algorithms. Although algorithms using OCT images appeared more homogeneous and had the highest summary specificity (0.97, 95% CI 0.93 to 0.98), they were not superior to algorithms using fundus images alone (0.94, 95% CI 0.89 to 0.97) or multimodal imaging (0.96, 95% CI 0.88 to 0.99; P for meta-regression = 0.239). The median prevalence of images with eAMD was 30% (interquartile range [IQR] 22% to 39%). We did not include eight studies that described nine algorithms (one study reported two sets of algorithm results) to distinguish eAMD from normal images, images of other AMD, or other non-AMD retinal lesions in the meta-analysis. Five of these algorithms were generally based on smaller datasets (range 21 to 218 participants per study) yet with a higher prevalence of eAMD images (range 33% to 66%). Relative to human graders, the reported sensitivity in these studies ranged from 0.95 and 0.97, while the specificity ranged from 0.94 to 0.99. Similarly, using small datasets (range 46 to 106), an additional four algorithms for detecting eAMD from other retinal lesions showed high sensitivity (range 0.96 to 1.00) and specificity (range 0.77 to 1.00).

AUTHORS' CONCLUSIONS

Low- to very low-certainty evidence suggests that an algorithm-based test may correctly identify most individuals with eAMD without increasing unnecessary referrals (false positives) in either the primary or the specialty care settings. There were significant concerns for applying the review findings due to variations in the eAMD prevalence in the included studies. In addition, among the included algorithm-based tests, diagnostic accuracy estimates were at risk of bias due to study participants not reflecting real-world characteristics, inadequate model validation, and the likelihood of selective results reporting. Limited quality and quantity of externally validated algorithms highlighted the need for high-certainty evidence. This evidence will require a standardized definition for eAMD on different imaging modalities and external validation of the algorithm to assess generalizability.

Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023)

BACKGROUND AND OBJECTIVES

Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases.

METHOD

The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review.

RESULTS

During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.

A novel approach for automatic classification of macular degeneration OCT images

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.

Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images

Age-related macular degeneration (AMD) is an eye disease that leads to the deterioration of the central vision area of the eye and can gradually result in vision loss in elderly individuals. Early identification of this disease can significantly impact patient treatment outcomes. Furthermore, given the increasing elderly population globally, the importance of automated methods for rapidly monitoring at-risk individuals and accurately diagnosing AMD is growing daily. One standard method for diagnosing AMD is using optical coherence tomography (OCT) images as a non-invasive imaging technology. In recent years, numerous deep neural networks have been proposed for the classification of OCT images. Utilizing pre-trained neural networks can speed up model deployment in related tasks without compromising accuracy. However, most previous methods overlook the feasibility of leveraging pre-existing trained networks to search for an optimal architecture for AMD staging on a new target dataset. In this study, our objective was to achieve an optimal architecture in the efficiency-accuracy trade-off for classifying retinal OCT images. To this end, we employed pre-trained medical vision transformer (MedViT) models. MedViT combines convolutional and transformer neural networks, explicitly designed for medical image classification. Our approach involved pre-training two distinct MedViT models on a source dataset with labels identical to those in the target dataset. This pre-training was conducted in a supervised manner. Subsequently, we evaluated the performance of the pre-trained MedViT models for classifying retinal OCT images from the target Noor Eye Hospital (NEH) dataset into the normal, drusen, and choroidal neovascularization (CNV) classes in zero-shot settings and through five-fold cross-validation. Then, we proposed a stitching approach to search for an optimal model from two MedViT family models. The proposed stitching method is an efficient architecture search algorithm known as stitchable neural networks. Stitchable neural networks create a candidate model in search space for each pair of stitchable layers by inserting a linear layer between them. A pair of stitchable layers consists of layers, each selected from one input model. While stitchable neural networks had previously been tested on more extensive and general datasets, this study demonstrated that stitching networks could also be helpful in smaller medical datasets. The results of this approach indicate that when pre-trained models were available for OCT images from another dataset, it was possible to achieve a model in 100 epochs with an accuracy of over 94.9% in classifying images from the NEH dataset. The results of this study demonstrate the efficacy of stitchable neural networks as a fine-tuning method for OCT image classification. This approach not only leads to higher accuracy but also considers architecture optimization at a reasonable computational cost.

A new computer-aided diagnosis tool based on deep learning methods for automatic detection of retinal disorders from OCT images

PURPOSE

Early detection of retinal disorders using optical coherence tomography (OCT) images can prevent vision loss. Since manual screening can be time-consuming, tedious, and fallible, we present a reliable computer-aided diagnosis (CAD) software based on deep learning. Also, we made efforts to increase the interpretability of the deep learning methods, overcome their vague and black box nature, and also understand their behavior in the diagnosis.

METHODS

We propose a novel method to improve the interpretability of the used deep neural network by embedding the rich semantic information of abnormal areas based on the ophthalmologists' interpretations and medical descriptions in the OCT images. Finally, we trained the classification network on a small subset of the online publicly available University of California San Diego (UCSD) dataset with an overall of 29,800 OCT images.

RESULTS

The experimental results on the 1000 test OCT images show that the proposed method achieves the overall precision, accuracy, sensitivity, and f1-score of 97.6%, 97.6%, 97.6%, and 97.59%, respectively. Also, the heat map images provide a clear region of interest which indicates that the interpretability of the proposed method is increased dramatically.

CONCLUSION

The proposed software can help ophthalmologists in providing a second opinion to make a decision, and primitive automated diagnoses of retinal diseases and even it can be used as a screening tool, in eye clinics. Also, the improvement of the interpretability of the proposed method causes to increase in the model generalization, and therefore, it will work properly on a wide range of other OCT datasets.

Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT

Macular pathologies can cause significant vision loss. Optical coherence tomography (OCT) images of the retina can assist ophthalmologists in diagnosing macular diseases. Traditional deep learning networks for retinal disease classification cannot extract discriminative features under strong noise conditions in OCT images. To address this issue, we propose a multi-scale-denoising residual convolutional network (MS-DRCN) for classifying retinal diseases. Specifically, the MS-DRCN includes a soft-denoising block (SDB), a multi-scale context block (MCB), and a feature fusion block (FFB). The SDB can determine the threshold for soft thresholding automatically, which removes speckle noise features efficiently. The MCB is designed to capture multi-scale context information and strengthen extracted features. The FFB is dedicated to integrating high-resolution and low-resolution features to precisely identify variable lesion areas. Our approach achieved classification accuracies of 96.4% and 96.5% on the OCT2017 and OCT-C4 public datasets, respectively, outperforming other classification methods. To evaluate the robustness of our method, we introduced Gaussian noise and speckle noise with varying PSNRs into the test set of the OCT2017 dataset. The results of our anti-noise experiments demonstrate that our approach exhibits superior robustness compared with other methods, yielding accuracy improvements ranging from 0.6% to 2.9% when compared with ResNet under various PSNR noise conditions.

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despite considerable progress in these algorithms, several limitations persist in medical imaging fields, where a lack of data is a common issue. Accordingly, specific image processing techniques, such as time-frequency transforms, can be employed in conjunction with AI algorithms to enhance diagnostic accuracy. This research investigates the influence of non-data-adaptive time-frequency transforms, specifically X-lets, on the classification of OCT B-scans. For this purpose, each B-scan was transformed using every considered X-let individually, and all the sub-bands were utilized as the input for a designed 2D Convolutional Neural Network (CNN) to extract optimal features, which were subsequently fed to the classifiers. Evaluating per-class accuracy shows that the use of the 2D Discrete Wavelet Transform (2D-DWT) yields superior outcomes for normal cases, whereas the circlet transform outperforms other X-lets for abnormal cases characterized by circles in their retinal structure (due to the accumulation of fluid). As a result, we propose a novel transform named CircWave by concatenating all sub-bands from the 2D-DWT and the circlet transform. The objective is to enhance the per-class accuracy of both normal and abnormal cases simultaneously. Our findings show that classification results based on the CircWave transform outperform those derived from original images or any individual transform. Furthermore, Grad-CAM class activation visualization for B-scans reconstructed from CircWave sub-bands highlights a greater emphasis on circular formations in abnormal cases and straight lines in normal cases, in contrast to the focus on irrelevant regions in original B-scans. To assess the generalizability of our method, we applied it to another dataset obtained from a different imaging system. We achieved promising accuracies of 94.5% and 90% for the first and second datasets, respectively, which are comparable with results from previous studies. The proposed CNN based on CircWave sub-bands (i.e. CircWaveNet) not only produces superior outcomes but also offers more interpretable results with a heightened focus on features crucial for ophthalmologists.

Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images

In recent decades, medical imaging techniques have revolutionized the field of disease diagnosis, enabling healthcare professionals to noninvasively observe the internal structures of the human body. Among these techniques, optical coherence tomography (OCT) has emerged as a powerful and versatile tool that allows high-resolution, non-invasive, and real-time imaging of biological tissues. Deep learning algorithms have been successfully employed to detect and classify various retinal diseases in OCT images, enabling early diagnosis and treatment planning. However, existing deep learning algorithms are primarily designed for single-disease diagnosis, which limits their practical application in clinical settings where OCT images often contain symptoms of multiple diseases. In this paper, we propose an effective approach for multi-disease diagnosis in OCT images using a multi-scale learning (MSL) method and a sparse residual network (SRN). Specifically, the MSL method extracts and fuses useful features from images of different sizes to enhance the discriminative capability of a classifier and make the disease predictions interpretable. The SRN is a minimal residual network, where convolutional layers with large kernel sizes are replaced with multiple convolutional layers that have smaller kernel sizes, thereby reducing model complexity while achieving a performance similar to that of existing convolutional neural networks. The proposed multi-scale sparse residual network significantly outperforms existing methods, exhibiting 97.40% accuracy, 95.38% sensitivity, and 98.25% specificity. Experimental results show the potential of our method to improve explainable diagnosis systems for various eye diseases via visual discrimination.

Robust AMD Stage Grading with Exclusively OCTA Modality Leveraging 3D Volume

Age-related Macular Degeneration (AMD) is a degenerative eye disease that causes central vision loss. Optical Coherence Tomography Angiography (OCTA) is an emerging imaging modality that aids in the diagnosis of AMD by displaying the pathogenic vessels in the subretinal space. In this paper, we investigate the effectiveness of OCTA from the view of deep classifiers. To the best of our knowledge, this is the first study that solely uses OCTA for AMD stage grading. By developing a 2D classifier based on OCTA projections, we identify that segmentation errors in retinal layers significantly affect the accuracy of classification. To address this issue, we propose analyzing 3D OCTA volumes directly using a 2D convolutional neural network trained with additional projection supervision. Our experimental results show that we achieve over 80% accuracy on a four-stage grading task on both error-free and error-prone test sets, which is significantly higher than 60%, the accuracy of human experts. This demonstrates that OCTA provides sufficient information for AMD stage grading and the proposed 3D volume analyzer is more robust when dealing with OCTA data with segmentation errors.

Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture

Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture's ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.

Improved Detection of Dry Age-Related Macular Degeneration from Optical Coherence Tomography Images using Adaptive Window Based Feature Extraction and Weighted Ensemble Based Classification Approach

BACKGROUND

Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task.

METHODOLOGY

This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer.

RESULT

The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset.

CONCLUSION

The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.

A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers

Background: Artificial intelligence (AI) is used in ophthalmological disease screening and diagnostics, medical image diagnostics, and predicting late-disease progression rates. We reviewed all AI publications associated with macular edema (ME) research Between 2011 and 2022 and performed modeling, quantitative, and qualitative investigations. Methods: On 1st February 2023, we screened the Web of Science Core Collection for AI applications related to ME, from which 297 studies were identified and analyzed (2011-2022). We collected information on: publications, institutions, country/region, keywords, journal name, references, and research hotspots. Literature clustering networks and Frontier knowledge bases were investigated using bibliometrix-BiblioShiny, VOSviewer, and CiteSpace bibliometric platforms. We used the R "bibliometrix" package to synopsize our observations, enumerate keywords, visualize collaboration networks between countries/regions, and generate a topic trends plot. VOSviewer was used to examine cooperation between institutions and identify citation relationships between journals. We used CiteSpace to identify clustering keywords over the timeline and identify keywords with the strongest citation bursts. Results: In total, 47 countries published AI studies related to ME; the United States had the highest H-index, thus the greatest influence. China and the United States cooperated most closely between all countries. Also, 613 institutions generated publications - the Medical University of Vienna had the highest number of studies. This publication record and H-index meant the university was the most influential in the ME field. Reference clusters were also categorized into 10 headings: retinal Optical Coherence Tomography (OCT) fluid detection, convolutional network models, deep learning (DL)-based single-shot predictions, retinal vascular disease, diabetic retinopathy (DR), convolutional neural networks (CNNs), automated macular pathology diagnosis, dry age-related macular degeneration (DARMD), class weight, and advanced DL architecture systems. Frontier keywords were represented by diabetic macular edema (DME) (2021-2022). Conclusion: Our review of the AI-related ME literature was comprehensive, systematic, and objective, and identified future trends and current hotspots. With increased DL outputs, the ME research focus has gradually shifted from manual ME examinations to automatic ME detection and associated symptoms. In this review, we present a comprehensive and dynamic overview of AI in ME and identify future research areas.

An interpretable transformer network for the retinal disease classification using optical coherence tomography

Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models' decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.

Deep learning algorithms for detection of diabetic macular edema in OCT images: A systematic review and meta-analysis

PURPOSE

Artificial intelligence (AI) can detect diabetic macular edema (DME) from optical coherence tomography (OCT) images. We aimed to evaluate the performance of deep learning neural networks in DME detection.

METHODS

Embase, Pubmed, the Cochrane Library, and IEEE Xplore were searched up to August 14, 2021. We included studies using deep learning algorithms to detect DME from OCT images. Two reviewers extracted the data independently, and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the risk of bias. The study is reported according to Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA).

RESULTS

Ninteen studies involving 41005 subjects were included. The pooled sensitivity and specificity were 96.0% (95% confidence interval (CI): 93.9% to 97.3%) and 99.3% (95% CI: 98.2% to 99.7%), respectively. Subgroup analyses found that data set selection, sample size of training set and the choice of OCT devices contributed to the heterogeneity (all P < 0.05). While there was no association between the diagnostic accuracy and transfer learning adoption or image management (all P > 0.05).

CONCLUSIONS

Deep learning methods, particularly the convolutional neural networks (CNNs) could effectively detect clinically significant DME, which can provide referral suggestions to the patients.

Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images

Nuclear cataract (NC) is a leading ocular disease globally for blindness and vision impairment. NC patients can improve their vision through cataract surgery or slow the opacity development with early intervention. Anterior segment optical coherence tomography (AS-OCT) image is an emerging ophthalmic image type, which can clearly observe the whole lens structure. Recently, clinicians have been increasingly studying the correlation between NC severity levels and clinical features from the nucleus region on AS-OCT images, and the results suggested the correlation is strong. However, automatic NC classification research based on AS-OCT images has rarely been studied. This paper presents a novel mixed pyramid attention network (MPANet) to classify NC severity levels on AS-OCT images automatically. In the MPANet, we design a novel mixed pyramid attention (MPA) block, which first applies the group convolution method to enhance the feature representation difference of feature maps and then construct a mixed pyramid pooling structure to extract local-global feature representations and different feature representation types simultaneously. We conduct extensive experiments on a clinical AS-OCT image dataset and a public OCT dataset to evaluate the effectiveness of our method. The results demonstrate that our method achieves competitive classification performance through comparisons to state-of-the-art methods and previous works. Moreover, this paper also uses the class activation mapping (CAM) technique to improve our method's interpretability of classification results.

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

Adaptive feature squeeze network for nuclear cataract classification in AS-OCT image

Nuclear cataract (NC) is an age-related cataract disease. Cataract surgery is an effective method to improve the vision and life quality of NC patients. Anterior segment optical coherence tomography (AS-OCT) images are noninvasive, reproductive, and easy-measured, which can capture opacity clearly on the lens nucleus region. However, automatic AS-OCT-based NC classification research has not been extensively studied. This paper proposes a novel convolutional neural network (CNN) framework named Adaptive Feature Squeeze Network (AFSNet) to classify NC severity levels automatically. In the AFSNet, we construct an adaptive feature squeeze module to dynamically squeeze local feature representations and update the relative importance of global feature representations, which is comprised of a squeeze block and a global adaptive pooling operation. We conduct comprehensive experiments on a clinical AS-OCT image dataset and a public OCT images dataset, and results demonstrate our method's effectiveness and superiority over strong baselines and previous state-of-the-art methods. Furthermore, this paper also demonstrates that CNNs achieve better NC classification results on the nucleus region than the lens region. We also adopt the class activation mapping (CAM) technique to localize the discriminative regions that CNN models learned, which enhances the interpretability of classification results.

Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD.

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.

A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images

BACKGROUND AND OBJECTIVE

One of the significant retinal diseases that affected older people is called Age-related Macular Degeneration (AMD). The first stage creates a blur effect on vision and later leads to central vision loss. Most people overlooked the primary stage blurring and converted it into an advanced stage. There is no proper treatment to cure the disease. So the early detection of AMD is essential to prevent its extension into the advanced stage. This paper proposes a novel deep Convolutional Neural Network (CNN) architecture to automate AMD diagnosis early from Optical Coherence Tomographic (OCT) images.

METHODS

The proposed architecture is a multiscale and multipath CNN with six convolutional layers. The multiscale convolution layer permits the network to produce many local structures with various filter dimensions. The multipath feature extraction permits CNN to merge more features regarding the sparse local and fine global structures. The performance of the proposed architecture is evaluated through ten-fold cross-validation methods using different classifiers like support vector machine, multi-layer perceptron, and random forest.

RESULTS

The proposed CNN with the random forest classifier gives the best classification accuracy results. The proposed method is tested on data set 1, data set 2, data set 3, data set 4, and achieved an accuracy of 0.9666, 0.9897, 0.9974, and 0.9978 respectively, with random forest classifier. Also, we tested the combination of first three data sets and achieved an accuracy of 0.9902.

CONCLUSIONS

An efficient algorithm for detecting AMD from OCT images is proposed based on a multiscale and multipath CNN architecture. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in the detection of AMD. The proposed architecture can be applied in rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters.

Non-transfer Deep Learning of Optical Coherence Tomography for Post-hoc Explanation of Macular Disease Classification

Deep transfer learning is a popular choice for classifying monochromatic medical images using models that are pretrained by natural images with color channels. This choice may introduce unnecessarily redundant model complexity that can limit explanations of such model behavior and outcomes in the context of medical imaging. To investigate this hypothesis, we develop a configurable deep convolutional neural network (CNN) to classify four macular disease conditions using retinal optical coherence tomography (OCT) images. Our proposed non-transfer deep CNN model (acc: 97.9%) outperforms existing transfer learning models such as ResNet-50 (acc: 89.0%), ResNet-101 (acc: 96.7%), VGG-19 (acc: 93.3%), Inception-V3 (acc: 95.8%) in the same retinal OCT image classification task. We perform post-hoc analysis of the trained model and model extracted image features, which reveals that only eight out of 256 filter kernels are active at our final convolutional layer. The convolutional responses of these selective eight filters yield image features that efficiently separate four macular disease classes even when projected onto two-dimensional principal component space. Our findings suggest that many deep learning parameters and their computations are redundant and expensive for retinal OCT image classification, which are expected to be more intense when using transfer learning. Additionally, we provide clinical interpretations of our misclassified test images identifying manifest artifacts, shadowing of useful texture, false texture representing fluids, and other confounding factors. These clinical explanations along with model optimization via kernel selection can improve the classification accuracy, computational costs, and explainability of model outcomes.

OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images

BACKGROUND AND OBJECTIVE

Retinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina.

METHODS

In the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm.

RESULTS

Training and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases.

CONCLUSION

In the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina's actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.