Deep learning-based hemorrhage detection for diabetic retinopathy screening

Application of Artificial Intelligence in the Diagnosis, Follow-Up and Prediction of Treatment of Ophthalmic Diseases

PURPOSE

To describe the application of artificial intelligence (AI) in ophthalmic diseases and its possible future directions.

METHODS

A retrospective review of the literature from PubMed, Web of Science, and Embase databases (2019-2024).

RESULTS

AI assists in cataract diagnosis, classification, preoperative lens calculation, surgical risk, postoperative vision prediction, and follow-up. For glaucoma, AI enhances early diagnosis, progression prediction, and surgical risk assessment. It detects diabetic retinopathy early and predicts treatment effects for diabetic macular edema. AI analyzes fundus images for age-related macular degeneration (AMD) diagnosis and risk prediction. Additionally, AI quantifies and grades vitreous opacities in uveitis. For retinopathy of prematurity, AI facilitates disease classification, predicting disease occurrence and severity. Recently, AI also predicts systemic diseases by analyzing fundus vascular changes.

CONCLUSIONS

AI has been extensively used in diagnosing, following up, and predicting treatment outcomes for common blinding eye diseases. In addition, it also has a unique role in the prediction of systemic diseases.

VisionGuard: enhancing diabetic retinopathy detection with hybrid deep learning

OBJECTIVES

Early detection of diabetic retinopathy (DR) and timely interventionare critical for preventing vision loss. Recently, deep learning techniqueshave shown promising results in streamlining this process. The objective ofthis study was to develop a novel method, termed MobileFusionNet, whichintegrates the strengths of MobileNet and GoogleNet architectures to automatethe detection of DR better using mobile devices.

METHODS

The model is implemented in Python and trained on large-scale datasetsof retinal images annotated with DR severity levels. The initial step involvespre-processing the images. Further, an advanced feature extraction techniquenamed Histogram of Oriented Gradients (HOG) is utilized, which helps capturethe shape/texture information. Finally, the methodology incorporates LinearDiscriminant Analysis (LDA), a technique aimed at reducing the dimensionalityof the extracted features.

RESULTS

The proposed modeldisplays low inference time and is highly energy efficient. The model exhibits highsensitivity and specificity in detecting DR, with an impressive accuracy of98.19%.

CONCLUSIONS

The model with its modular architecture allows easy integration and holdsgreat potential for revolutionizing DR detection by democratizing access toaccurate and timely screening, particularly in resource-limited settings.

Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis

BACKGROUND

In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy.

METHODS

This study was conducted based on the PRISMA checklist. We searched online databases, including PubMed, Scopus, and Google Scholar, for relevant articles up to September 30, 2023. After the title, abstract, and full-text screening, data extraction and quality assessment were done for the included studies. Finally, a meta-analysis was performed.

RESULTS

We included 76 studies with a total of 1,371,517 retinal images, of which 51 were used for meta-analysis. Our meta-analysis showed a significant sensitivity and specificity with a percentage of 90.54 (95%CI [90.42, 90.66], P < 0.001) and 78.33% (95%CI [78.21, 78.45], P < 0.001). However, the AUC (area under curvature) did not statistically differ across studies, but had a significant figure of 0.94 (95% CI [- 46.71, 48.60], P = 1).

CONCLUSIONS

Although machine learning and deep learning algorithms can properly diagnose diabetic retinopathy, their discriminating capacity is limited. However, they could simplify the diagnosing process. Further studies are required to improve algorithms.

Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets

Diabetes Mellitus (DM) is a global health challenge, and accurate early detection is critical for effective management. The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. Researchers utilizing a hybrid feature extraction method such as Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) followed by metaheuristic feature selection algorithms as Harmonic Search (HS), Dragonfly Algorithm (DFA), Elephant Herding Algorithm (EHA). Evaluated the performance of a system by using the following classifiers as Non-Linear Regression-NLR, Linear Regression-LR, Gaussian Mixture Model-GMM, Expectation Maximization-EM, Bayesian Linear Discriminant Analysis-BLDA, Softmax Discriminant Classifier-SDC, and Support Vector Machine with Radial Basis Function kernel-SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). The findings demonstrate significant improvement in classification accuracy compared to using all genes. On the Nordic islet transplant dataset, the combined ABC-PSO feature extraction with EHO feature selection achieved the highest accuracy of 97.14%, surpassing the 94.28% accuracy obtained with ABC alone and EHO selection. Similarly, on the PIMA Indian diabetes dataset, the ABC-PSO and EHO combination achieved the best accuracy of 98.13%, exceeding the 95.45% accuracy with ABC and DFA selection. These results highlight the effectiveness of our proposed approach in identifying the most informative features for accurate diabetes prediction. It is observed that the parametric values attained for the datasets are almost similar. Therefore, this research indicates the robustness of the FE and FS along with classifier techniques with two different datasets.

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net

Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an updated UNet framework, adept at scrutinizing fundus images for signs of retinal haemorrhages. The customized UNet underwent GPU training using the IDRiD database, validated against the publicly available DIARETDB1 and IDRiD datasets. Emphasizing the complexity of segmentation, the study employed preprocessing techniques, augmenting image quality and data integrity. Subsequently, the trained neural network showcased a remarkable performance boost, accurately identifying haemorrhage regions with 80% sensitivity, 99.6% specificity, and 98.6% accuracy. The experimental findings solidify the network's reliability, showcasing potential to alleviate ophthalmologists' workload significantly. Notably, achieving an Intersection over Union (IoU) of 76.61% and a Dice coefficient of 86.51% underscores the system's competence. The study's outcomes signify substantial enhancements in diagnosing critical diabetic retinal conditions, promising profound improvements in diagnostic accuracy and efficiency, thereby marking a significant advancement in automated retinal haemorrhage detection for diabetic retinopathy.

A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification

Diabetic retinopathy (DR) is a retinal disease caused by diabetes. If there is no intervention, it may even lead to blindness. Therefore, the detection of diabetic retinopathy is of great significance for preventing blindness in patients. Most of the existing DR detection methods use supervised methods, which usually require a large number of accurate pixel-level annotations. To solve this problem, we propose a self-supervised Equivariant Refinement Classification Network (ERCN) for DR classification. First, we use an unsupervised contrast pre-training network to learn a more generalized representation. Secondly, the class activation map (CAM) is refined by self-supervision learning. It first uses a spatial masking method to suppress low-confidence predictions, and then uses the feature similarity between pixels to encourage fine-grained activation to achieve more accurate positioning of the lesion. We propose a hybrid equivariant regularization loss to alleviate the degradation caused by the local minimum in the CAM refinement process. To further improve the classification accuracy, we propose an attention-based multi-instance learning (MIL), which weights each element of the feature map as an instance, which is more effective than the traditional patch-based instance extraction method. We evaluate our method on the EyePACS and DAVIS datasets and achieved 87.4% test accuracy in the EyePACS dataset and 88.7% test accuracy in the DAVIS dataset. It shows that the proposed method achieves better performance in DR detection compared with other state-of-the-art methods in self-supervised DR detection.

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina

Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, necessitates the precise segmentation of lesions for the effective grading of lesions. DR multi-lesion segmentation faces the main concerns as follows. On the one hand, retinal lesions vary in location, shape, and size. On the other hand, the currently available multi-lesion region segmentation models are insufficient in their extraction of minute features and are prone to overlooking microaneurysms. To solve the above problems, we propose a novel deep learning method: the Multi-Scale Spatial Attention Gate (MSAG) mechanism network. The model inputs images of varying scales in order to extract a range of semantic information. Our innovative Spatial Attention Gate merges low-level spatial details with high-level semantic content, assigning hierarchical attention weights for accurate segmentation. The incorporation of the modified spatial attention gate in the inference stage enhances precision by combining prediction scales hierarchically, thereby improving segmentation accuracy without increasing the associated training costs. We conduct the experiments on the public datasets IDRiD and DDR, and the experimental results show that the proposed method achieves better performance than other methods.

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.

SSMD-UNet: semi-supervised multi-task decoders network for diabetic retinopathy segmentation

Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.