Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography

Interpretable multimodal classification for age-related macular degeneration diagnosis

Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to "unbox" the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.

A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical Imaging

The combination of medical imaging and deep learning has significantly improved diagnostic and prognostic capabilities in the healthcare domain. Nevertheless, the inherent complexity of deep learning models poses challenges in understanding their decision-making processes. Interpretability and visualization techniques have emerged as crucial tools to unravel the black-box nature of these models, providing insights into their inner workings and enhancing trust in their predictions. This survey paper comprehensively examines various interpretation and visualization techniques applied to deep learning models in medical imaging. The paper reviews methodologies, discusses their applications, and evaluates their effectiveness in enhancing the interpretability, reliability, and clinical relevance of deep learning models in medical image analysis.

Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy

Diabetic retinopathy is one of the leading causes of blindness around the world. This makes early diagnosis and treatment important in preventing vision loss in a large number of patients. Microaneurysms are the key hallmark of the early stage of the disease, non-proliferative diabetic retinopathy, and can be detected using OCT angiography quickly and non-invasively. Screening tools for non-proliferative diabetic retinopathy using OCT angiography thus have the potential to lead to improved outcomes in patients. We compared different configurations of ensembled U-nets to automatically segment microaneurysms from OCT angiography fundus projections. For this purpose, we created a new database to train and evaluate the U-nets, created by two expert graders in two stages of grading. We present the first U-net neural networks using ensembling for the detection of microaneurysms from OCT angiography en face images from the superficial and deep capillary plexuses in patients with non-proliferative diabetic retinopathy trained on a database labeled by two experts with repeats.

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.

RD-OCT net: hybrid learning system for automated diagnosis of macular diseases from OCT retinal images

Macular Edema is a leading cause of visual impairment and blindness in patients with ocular fundus diseases. Due to its non-invasive and high-resolution characteristics, optical coherence tomography (OCT) has been extensively utilized for the diagnosis of macular diseases. The manual detection of retinal diseases by clinicians is a laborious process, further complicated by the challenging identification of macular diseases. This difficulty arises from the significant pathological alterations occurring within the retinal layers, as well as the accumulation of fluid in the retina. Deep Learning neural networks are utilized for automatic detection of retinal diseases. This paper aims to propose a lightweight hybrid learning Retinal Disease OCT Net with a reduced number of trainable parameters and enable automatic classification of retinal diseases. A Hybrid Learning Retinal Disease OCT Net (RD-OCT) is utilized for the multiclass classification of major retinal diseases, namely neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME), retinal vein occlusion (RVO), and normal retinal conditions. The diagnosis of retinal diseases is facilitated by the use of hybrid learning models and pre-trained deep learning models in the field of artificial intelligence. The Hybrid Learning RD-OCT Net provides better accuracy of 97.6% for nAMD, 98.08% for DME, 98% for RVO, and 97% for the Normal group. The respective area under the curve values were 0.99, 0.97, 1.0, and 0.99. The utilization of the RD-OCT model will be useful for ophthalmologists in the diagnosis of prevalent retinal diseases, due to the simplicity of the system and reduced number of trainable parameters.

Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt

(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden. Diabetic macular edema (DME) is a severe complication of DM that can be prevented if it is timely screened with the help of optical coherence tomography (OCT) devices. Newly developing state-of-the-art artificial intelligence (AI) algorithms can assist physicians in analyzing large datasets and flag potential risks. By using AI algorithms in order to process OCT images of large populations, the screening capacity and speed can be increased so that patients can be timely treated. This quick response gives the physicians a chance to intervene and prevent disability. (2) Methods: This study evaluated ConvNeXt and EfficientNet architectures in correctly identifying DME patterns on real-life OCT images for screening purposes. (3) Results: Firstly, we obtained models that differentiate between diabetic retinopathy (DR) and healthy scans with an accuracy of 0.98. Secondly, we obtained a model that can indicate the presence of edema, detachment of the subfoveolar neurosensory retina, and hyperreflective foci (HF) without using pixel level annotation. Lastly, we analyzed the extent to which the pretrained weights on natural images "understand" OCT scans. (4) Conclusions: Pretrained networks such as ConvNeXt or EfficientNet correctly identify features relevant to the differentiation between healthy retinas and DR, even though they were pretrained on natural images. Another important aspect of our research is that the differentiation between biomarkers and their localization can be obtained even without pixel-level annotation. The "three biomarkers model" is able to identify obvious subfoveal neurosensory detachments, retinal edema, and hyperreflective foci, as well as very small subfoveal detachments. In conclusion, our study points out the possible usefulness of AI-assisted diagnosis of DME for lowering healthcare costs, increasing the quality of life of patients with diabetes, and reducing the waiting time until an appropriate ophthalmological consultation and treatment can be performed.

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images

Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named "TurkerNeXt". This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder.

Few-shot out-of-distribution detection for automated screening in retinal OCT images using deep learning

Deep neural networks have been increasingly proposed for automated screening and diagnosis of retinal diseases from optical coherence tomography (OCT), but often provide high-confidence predictions on out-of-distribution (OOD) cases, compromising their clinical usage. With this in mind, we performed an in-depth comparative analysis of the state-of-the-art uncertainty estimation methods for OOD detection in retinal OCT imaging. The analysis was performed within the use-case of automated screening and staging of age-related macular degeneration (AMD), one of the leading causes of blindness worldwide, where we achieved a macro-average area under the curve (AUC) of 0.981 for AMD classification. We focus on a few-shot Outlier Exposure (OE) method and the detection of near-OOD cases that share pathomorphological characteristics with the inlier AMD classes. Scoring the OOD case based on the Cosine distance in the feature space from the penultimate network layer proved to be a robust approach for OOD detection, especially in combination with the OE. Using Cosine distance and only 8 outliers exposed per class, we were able to improve the near-OOD detection performance of the OE with Reject Bucket method by [Formula: see text] 10% compared to without OE, reaching an AUC of 0.937. The Cosine distance served as a robust metric for OOD detection of both known and unknown classes and should thus be considered as an alternative to the reject bucket class probability in OE approaches, especially in the few-shot scenario. The inclusion of these methodologies did not come at the expense of classification performance, and can substantially improve the reliability and trustworthiness of the resulting deep learning-based diagnostic systems in the context of retinal OCT.

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%.

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia. Delayed diagnosis and overpriced tests are the prime reason for the high mortality rate, especially in underdeveloped countries. A time and cost-efficient diagnosis tool: Chest X-rays, was thus accepted as the standard diagnostic test for pediatric pneumonia. However, the lower radiation levels for diagnosis in children make the task much more onerous and time-consuming. The mentioned challenges initiate the need for a computer-aided detection model that is instantaneous and accurate. Our work proposes a stacked ensemble learning of deep learning-based features for pediatric pneumonia classification. The extracted features from the global average pooling layer of the fine-tuned Xception model pretrained on ImageNet weights are sent to the Kernel Principal Component Analysis for dimensionality reduction. The dimensionally reduced features are further trained and validated on the stacking classifier. The stacking classifier consists of two stages; the first stage uses the Random-Forest classifier, K-Nearest Neighbors, Logistic Regression, XGB classifier, Support Vector Classifier (SVC), Nu-SVC, and MLP classifier. The second stage operates on Logistic Regression using the first stage predictions for the final classification with Stratified K-fold cross-validation to prevent overfitting. The model was tested on the publicly available pediatric pneumonia dataset, achieving an accuracy of 98.3%, precision of 99.29%, recall of 98.36%, F1-score of 98.83%, and an AUC score of 98.24%. The performance shows its reliability for real-time deployment in assisting radiologists and physicians.

Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures

Pediatric pneumonia has drawn immense awareness due to the high mortality rates over recent years. The acute respiratory infection caused by bacteria, viruses, or fungi infects the lung region and hinders oxygen transport, making breathing difficult due to inflamed or pus and fluid-filled alveoli. Being non-invasive and painless, chest X-rays are the most common modality for pediatric pneumonia diagnosis. However, the low radiation levels for diagnosis in children make accurate detection challenging. This challenge initiates the need for an unerring computer-aided diagnosis model. Our work proposes Contrast Limited Adaptive Histogram Equalization for image enhancement and a stacking classifier based on the fusion of deep learning-based features for pediatric pneumonia diagnosis. The extracted features from the global average pooling layers of the fine-tuned MobileNet, DenseNet121, DenseNet169, and DenseNet201 are concatenated for the final classification using a stacked ensemble classifier. The stacking classifier uses Support Vector Classifier, Nu-SVC, Logistic Regression, K-Nearest Neighbor, Random Forest Classifier, Gaussian Naïve Bayes, AdaBoost classifier, Bagging Classifier, and Extra-trees Classifier for the first stage, and Nu-SVC as the meta-classifier. The stacking classifier validated using Stratified K-Fold cross-validation achieves an accuracy of 98.62%, precision of 98.99%, recall of 99.53%, F1 score of 99.26%, and an AUC score of 93.17% on the publicly available pediatric pneumonia dataset. We expect this model to greatly help the real-time diagnosis of pediatric pneumonia.

Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer learning

Purpose

To apply deep learning (DL) techniques to develop an automatic intelligent classification system identifying the specific types of myopic maculopathy (MM) based on macular optical coherence tomography (OCT) images using transfer learning (TL).

Method

In this retrospective study, a total of 3,945 macular OCT images from 2,866 myopic patients were recruited from the ophthalmic outpatients of three hospitals. After culling out 545 images with poor quality, a dataset containing 3,400 macular OCT images was manually classified according to the ATN system, containing four types of MM with high OCT diagnostic values. Two DL classification algorithms were trained to identify the targeted lesion categories: Algorithm A was trained from scratch, and algorithm B using the TL approach initiated from the classification algorithm developed in our previous study. After comparing the training process, the algorithm with better performance was tested and validated. The performance of the classification algorithm in the test and validation sets was evaluated using metrics including sensitivity, specificity, accuracy, quadratic-weighted kappa score, and the area under the receiver operating characteristic curve (AUC). Moreover, the human-machine comparison was conducted. To better evaluate the algorithm and clarify the optimization direction, the dimensionality reduction analysis and heat map analysis were also used to visually analyze the algorithm.

Results

Algorithm B showed better performance in the training process. In the test set, the algorithm B achieved relatively robust performance with macro AUC, accuracy, and quadratic-weighted kappa of 0.986, 96.04% (95% CI: 0.951, 0.969), and 0.940 (95% CI: 0.909-0.971), respectively. In the external validation set, the performance of algorithm B was slightly inferior to that in the test set. In human-machine comparison test, the algorithm indicators were inferior to the retinal specialists but were the same as the ordinary ophthalmologists. In addition, dimensionality reduction visualization and heatmap visualization analysis showed excellent performance of the algorithm.

Conclusion

Our macular OCT image classification algorithm developed using the TL approach exhibited excellent performance. The automatic diagnosis system for macular OCT images of MM based on DL showed potential application prospects.

Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning

Brain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classifiers are fed with a set of these features. Random Forest classifier offered the best performance with 94.51% accuracy, 91.61% sensitivity, 8.39% false-negative rate, 97.42% specificity, and 97.29% precision using fivefold cross-validation in this study. Consequently, it is thought that the proposed model could contribute to field experts by integrating it into computer-aided brain magnetic resonance imaging diagnosis systems.

Identification of Sex and Age from Macular Optical Coherence Tomography and Feature Analysis Using Deep Learning

PURPOSE

To develop deep learning models for identification of sex and age from macular optical coherence tomography (OCT) and to analyze the features for differentiation of sex and age.

DESIGN

Algorithm development using database of macular OCT.

METHODS

We reviewed 6147 sets of macular OCT images from the healthy eyes of 3134 individuals from a single eye center in Taiwan. Deep learning-based algorithms were used to develop models for the identification of sex and age, and 10-fold cross-validation was applied. Gradient-weighted class activation mapping was used for feature analysis.

RESULTS

The accuracy for sex prediction using deep learning from macular OCT was 85.6% ± 2.1% compared with accuracy of 61.9% using macular thickness and 61.4% ± 4.0% using deep learning from infrared fundus photography (P < .001 for both). The mean absolute error for age prediction using deep learning from macular OCT was 5.78 ± 0.29 years. A thorough analysis of the prediction accuracy and the gradient-weighted class activation mapping showed that the cross-sectional foveal contour lead to a better sex distinction than macular thickness or fundus photography, and the age-related characteristics of macula were on the whole layers of retina rather than the choroid.

CONCLUSIONS

Sex and age could be identified from macular OCT using deep learning with good accuracy. The main sexual difference of macula lies in the foveal contour, and the whole layers of retina differ with aging. These novel findings provide useful information for further investigation in the pathogenesis of sex- and age-related macular structural diseases.

Effect of optical coherence tomography and angiography sampling rate towards diabetic retinopathy severity classification

Optical coherence tomography (OCT) and OCT angiography (OCT-A) may benefit the screening of diabetic retinopathy (DR). This study investigated the effect of laterally subsampling OCT/OCT-A en face scans by up to a factor of 8 when using deep neural networks for automated referable DR classification. There was no significant difference in the classification performance across all evaluation metrics when subsampling up to a factor of 3, and only minimal differences up to a factor of 8. Our findings suggest that OCT/OCT-A can reduce the number of samples (and hence the acquisition time) for a volume for a given field of view on the retina that is acquired for rDR classification.

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure. Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI. Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

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.

Identifying Diabetic Macular Edema and Other Retinal Diseases by Optical Coherence Tomography Image and Multiscale Deep Learning

PURPOSE

Diabetic Macular Edema has been one of the research hotspots all over the world. But as the global population continues to grow, the number of OCT images requiring manual analysis is becoming increasingly unaffordable. Medical images are often fuzzy due to the inherent physical processes of acquiring them. It is difficult for traditional algorithms to use low-quality data. And traditional algorithms usually only provide diagnostic results, which makes the reliability and interpretability of the model face challenges. To solve problem above, we proposed a more intuitive and robust diagnosis model with self-enhancement ability and clinical triage patients' ability.

METHODS

We used 38,057 OCT images (Drusen, DME, CNV and Normal) to establish and evaluate the model. All data are OCT images of fundus retina. There were 37,457 samples in the training dataset and 600 samples in the validation dataset. In order to diagnose these images accurately, we propose a multiscale transfer learning algorithm. Firstly, the sample is sent to the automatic self-enhancement module for edge detection and enhancement. Then, the processed data are sent to the image diagnosis module to determine the disease type. This process makes more data more effective and can be accurately classified. Finally, we calculated the accuracy, precision, sensitivity and specificity of the model, and verified the performance of the model from the perspective of clinical application.

RESULTS

The model proposed in this paper can provide the diagnosis results and display the detection targets more intuitively. The model reached 94.5% accuracy, 97.2% precision, 97.7% sensitivity and 97% specificity in the independent testing dataset.

CONCLUSION

Comparing the performance of relevant work and ablation test, our model achieved relatively good performance. It is proved that the model proposed in this paper has a stronger ability to recognize diseases even in the face of low-quality images. Experiment results also demonstrate its clinical referral capability. It can reduce the workload of medical staff and save the precious time of patients.

Diving Deep into Deep Learning: An Update on Artificial Intelligence in Retina

Purpose of Review

In the present article, we will provide an understanding and review of artificial intelligence in the subspecialty of retina and its potential applications within the specialty.

Recent Findings

Given the significant use of diagnostic imaging within retina, this subspecialty is a fitting area for the incorporation of artificial intelligence. Researchers have aimed at creating models to assist in the diagnosis and management of retinal disease as well as in the prediction of disease course and treatment response. Most of this work thus far has focused on diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity, although other retinal diseases have started to be explored as well.

Summary

Artificial intelligence is well-suited to transform the practice of ophthalmology. A basic understanding of the technology is important for its effective implementation and growth.