Identification Of Diabetic Retinopathy Research Articles

Optical Imaging for Diabetic Retinopathy Diagnosis And Detection Using Ensemble Models

Diabetes, characterized by heightened blood sugar levels, can lead to a condition called Diabetic Retinopathy (DR), which adversely impacts the eyes due to elevated blood sugar affecting the retinal blood vessels. The most common cause of blindness in diabetics is thought to be Diabetic Retinopathy (DR), particularly in working-age individuals living in poor nations. People with type 1 or type 2 diabetes may develop this illness, and the risk rises with the length of diabetes and inadequate blood sugar management. There are limits to traditional approaches for the early identification of diabetic retinopathy (DR). In order to diagnose diabetic retinopathy, a model based on Convolutional neural network (CNN) is used in a unique way in this research. The suggested model uses a number of deep learning (DL) models, such as VGG19, Resnet50, and InceptionV3, to extract features. After concatenation, these characteristics are sent through the CNN algorithm for classification. By combining the advantages of several models, ensemble approaches can be effective tools for detecting diabetic retinopathy and increase overall performance and resilience. Classification and image recognition are just a few of the tasks that may be accomplished with ensemble approaches like combination of VGG19,Inception V3 and Resnet 50 to achieve high accuracy. The proposed model is evaluated using a publicly accessible collection of fundus images.VGG19, ResNet50, and InceptionV3 differ in their neural network architectures, feature extraction capabilities, object detection methods, and approaches to retinal delineation. VGG19 may excel in capturing fine details, ResNet50 in recognizing complex patterns, and InceptionV3 in efficiently capturing multi-scale features. Their combined use in an ensemble approach can provide a comprehensive analysis of retinal images, aiding in the delineation of retinal regions and identification of abnormalities associated with diabetic retinopathy. For instance, micro aneurysms, the earliest signs of DR, often require precise detection of subtle vascular abnormalities. VGG19′s proficiency in capturing fine details allows for the identification of these minute changes in retinal morphology. On the other hand, ResNet50′s strength lies in recognizing intricate patterns, making it effective in detecting neo neo-vascularization and complex hemorrhagic lesions. Meanwhile, InceptionV3′s multi-scale feature extraction enables comprehensive analysis, crucial for assessing macular edema and ischemic changes across different retinal layers.

Data-driven 2D-EWT based diabetic retinopathy identification using hybrid neural network

Diabetic retinopathy (DR) is one of the primary reason of blindness over the globe. During DR, retina deteriorates continuously with the presence of lesions. It is difficult to detect DR in early stage and manual diagnosis is resource-intensive. To full-fill the requirement, two-dimensional empirical wavelet transform (2D-EWT) and composite neural network based automated DR diagnosis system is developed. The informative part of fundus image is extracted from the given image. The 2D-EWT computes different number of boundaries in magnitude spectrum followed by different modes. Therefore, a data-driven uniform boundary identification method is suggested for DR classification purpose. The acquired decomposed modes are given to the composite neural network for classification. The developed system is assessed on three different datasets MESSIDOR-1, MESSIDOR-2, and AsiaPacific Tele Ophthalmology Society (APTOS) dataset on which proposed method achieved 75%, 83.82%, and 89.28% accuracy, respectively. Efficacy of the proposed system is superior than the compared methods in terms of accuracy, area under the curve, precision, specificity, and sensitivity.

OctoVision: A Smart System for Diabetic Retinopathy Disease Detection

Diabetic retinopathy (DR) is the major cause of vision impairment and blindness in diabetics. Early detection and treatments are critical in preventing irreparable retinal damage. Manual detection of diabetic retinopathy by an ophthalmologist takes a long time, and patients must suffer greatly during this time. This paper presents an automated approach for rapid DR detection using the DenseNet-121 architecture. Our model achieves an accuracy exceeding 80%, with a precision score of 81% and a recall score of 86%, indicating its high effectiveness in detecting DR. Additionally, we developed a server-based implementation where the trained model is deployed. Images captured by a camera are uploaded to a cloud server, which processes them and sends back a diagnostic response. This study contributes to continuing efforts to create efficient and reliable techniques for early DR identification, resulting in earliermanagement and better patient outcomes.

Diabetic retinopathy identification based on multi-source-free domain adaptation.

To address the challenges of data labeling difficulties, data privacy, and necessary large amount of labeled data for deep learning methods in diabetic retinopathy (DR) identification, the aim of this study is to develop a source-free domain adaptation (SFDA) method for efficient and effective DR identification from unlabeled data. A multi-SFDA method was proposed for DR identification. This method integrates multiple source models, which are trained from the same source domain, to generate synthetic pseudo labels for the unlabeled target domain. Besides, a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances. Validation is performed using three color fundus photograph datasets (APTOS2019, DDR, and EyePACS). The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks. It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains. The multi-SFDA method provides an effective approach to overcome the challenges in DR identification. The method not only addresses difficulties in data labeling and privacy issues, but also reduces the need for large amounts of labeled data required by deep learning methods, making it a practical tool for early detection and preservation of vision in diabetic patients.

Improving the Identification of Diabetic Retinopathy and Related Conditions in the Electronic Health Record Using Natural Language Processing Methods

Objective or PurposeTo compare the performance of three phenotyping methods in identifying diabetic retinopathy (DR) and related clinical conditions. DesignThree phenotyping methods were used to identify clinical conditions including unspecified DR, non-proliferative diabetic retinopathy (NPDR) (mild, moderate, severe), consolidated NPDR (unspecified DR or any NPDR), proliferative diabetic retinopathy (PDR), diabetic macular edema (DME), vitreous hemorrhage (VH), retinal detachment (RD) (tractional RD or combined tractional and rhegmatogenous RD), and neovascular glaucoma (NVG). The first method used only International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes (ICD-10 Lookup System). The next two methods used a Bidirectional Encoder Representations from Transformers (BERT) with a dense Multilayer Perceptron (MLP) output layer natural language processing (NLP) framework. The NLP framework was applied either to free-text of provider notes (Text-Only NLP System) or both free-text and ICD-10 diagnosis codes (Text-and-ICD NLP System). Subjects, Participants, and/or ControlsAdults ≥18 years with diabetes mellitus seen at the Wilmer Eye Institute. Methods, Intervention or TestingWe compared the performance of the three phenotyping methods in identifying the diabetic retinopathy related conditions with gold standard chart review. We also compared the estimated disease prevalence using each method. Main Outcome MeasuresPerformance of each method was reported as the macro F1 score. The agreement between the methods was calculated using the kappa statistic. Prevalence estimates were also calculated for each method. ResultsA total of 91,097 patients and 692,486 office visits were included in the study. Compared to the gold standard, the Text-and-ICD NLP System had the highest F1 score for most clinical conditions (range 0.39 to 0.64). The agreement between the ICD-10 Lookup System and Text-Only NLP System varied (kappa of 0.21 to 0.81). The prevalence of diabetic retinopathy and related conditions ranged from 1.1% for NVG to 17.9% for DME (using the Text-and-ICD NLP System). ConclusionsThe prevalence of diabetic retinopathy and related conditions varied significantly depending on the methodology of identifying cases. The best performing phenotyping method was the Text-and-ICD NLP System that used information in both diagnosis codes as well as free-text notes.

Automatic detection of microaneurysms using DeTraC deep convolutional neural network classifier with woodpecker mating algorithm

ABSTRACT Diabetic Retinopathy (DR) is a microvascular complication of diabetes that leads to visual blindness. Early identification of DR can prevent the loss of sight. The first visible sign of DR is the appearance of micro aneurysms (MAs). Micro aneurysms are seen as small red circular spots on the retinal surface. The very small size of micro aneurysms proves to be challenging in its proper detection. In this research, DeTrac Deep Convolutional Neural Network m classifier with Woodpecker Mating Algorithm is proposed for the detection of MAs. By using this technique, every image is classified as either MAs or non-MAs pixel to automatically detect micro aneurysms from the retinal images. Experimental results are evaluated on diabetic-retinopathy-detection (DRD) dataset of the Kaggle website. Extensive simulations on this dataset shows an improved performance over the existing methods with 0.98 mean sensitivity, 0.97 mean specificity, and 0.98 mean accuracy in detecting the MAs irrespective of their intrinsic properties.

A hybrid approach with customized machine learning classifiers and multiple feature extractors for enhancing diabetic retinopathy detection

Diabetic retinopathy (DR) is a severe global issue causing blindness if untreated, affecting millions worldwide and worsening over time. Addressing this growing concern necessitates early and accurate DR identification. This study introduces a novel approach to DR detection, combining machine learning algorithms with deep feature extraction techniques. A hybrid model is proposed by stacking predictions from diverse classifiers, such as Decision Trees, Random Forests, Support Vector Machines (SVMs), and more. Three deep learning models – MobileNetV2, DenseNet121, and InceptionResNetV2 – are employed as feature extractors from retinal images. Each classifier undergoes hyperparameter tuning for optimal performance. Using the APTOS 2019 Blindness Detection dataset, including preprocessing techniques like data augmentation and standardization, this hybrid model demonstrates promising accuracy in multi-class (95.50%) and binary classification (98.36%). Notably, DenseNet121 outperforms others. The results suggest the effectiveness of this hybrid technique in early diabetic retinopathy detection, holding significant promise for improved medical intervention.

Multiple Retinal Diseases Prediction for Enhancing the Identification of Diabetic Retinopathy

The primary causes of vision impairment and blindness are retinal diseases, which include diabetic retinopathy, age-related macular degeneration, glaucoma, and retinal detachment. Correct and timely diagnosis of these illnesses is essential for efficient treatment and patient care. This abstract describes a novel use of convolutional neural networks (CNNs) for the diagnosis and prediction of various retinal diseases. A large dataset of retinal images covering a variety of retinal diseases is gathered and labelled with disease names in this study. To guarantee consistency and improve the model's capacity to pick up pertinent features, these photos go through a thorough preprocessing process. Techniques for data augmentation are used to diversify datasets more. The architecture of a CNN is intended for the categorization of retinal disorders. Convolutional layers are used in this architecture to extract features, and pooling layers are used to reduce dimensionality. Fully connected layers are then used to classify diseases. Using supervised learning methods, the model is trained on the annotated dataset, optimizing the loss function and keeping an eye on validation performance to avoid overfitting. On a different test dataset, the CNN model's performance is evaluated using a number of evaluation metrics, such as accuracy, precision, recall, F1-score, and the AUC-ROC score. Additionally, post-processing steps are used to eliminate predictions with low confidence, increasing the model's clinical usefulness.

Enhanced Diabetic Retinopathy Diagnosis: A Comparative Analysis of models with an Ensemble classifier and Deep Q Learning

- A novel method that combines the strengths of different classifiers such as Naive Bayes, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) is introduced in this paper. This method tackles the urgent need for cutting-edge diagnostic methods in the field of ophthalmology, mainly for the identification of diabetic retinopathy (DR). The approach is ensemble-based. Classical methods in retinal analysis of images often fail as they are static and are unable to adjust to the unique details that each distinct image presents. This constraint results in less accurate and precise diagnostic results, highlighting the urgent need for more adaptable and dynamic methods. The suggested model differs significantly from previous methods. Through the use of an ensemble approach, it capitalizes on the distinct advantages of each classifier: the MLP process's sophisticated feature extraction skill, Naive Bayes' probabilistic analysis, and SVM's non-linear pattern recognition capacity. By combining these techniques, the inherent drawbacks of utilizing a single strategy are addressed, guaranteeing a more thorough examination of retinal samples and images. The core of this idea is the system using Deep Q Learning (DQL) for adaptive classifier selection. Using learned Q Values for various contexts, this reinforcement learning technique selects the best classifier adaptively for each unique retinal image, hence optimizing the ensemble. This approach not only advances diagnosis accuracy and precision but also guarantees ongoing learning and adaptation to keep up with changing data patterns and advances in imaging technology. Extensive experiments on the IDRiD & EyePACS Dataset show the effectiveness of this model with a 5.5% increase in overall accuracy with other performance metrics, the results show a significant improvement over the current method. They represent a significant advancement in the timely and accurate identification of diabetic retinopathy, which will ultimately benefit patients and lessen the strain on healthcare systems. Thus, this work represents a major step forward in patient care as well as a technological advance, opening the door to more efficient supervision and treatment of retinal illnesses.

An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy.

Micro aneurysm detection using optimized residual-based temporal attention Convolutional Neural Network with Inception-V3 transfer learning.

In this manuscript micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) with Inception-V3 transfer learning optimized with equilibrium optimization algorithm (MA-RTCNN-Inception V3-EOA) is proposed. The proposed research work contains four phases: (1) pre-processing, (2) segmentation, (3) post-processing, and (4) classification. At first, guided box filtering for contrast enhancement and background exclusion of input image. The proposed MA-RTCNN-Inception V3-EOA based classification framework is implemented in MATLAB using several performances evaluating metrics like precision, sensitivity, f-measure, specificity, accuracy, classification error rate, and Matthews's correlation coefficient and RoC analysis. The experimental outcome demonstrates that the proposed method provides 23.56%, 14.99%, and 21.37% higher accuracy and 31.26%, 57.69%, and 21.14% minimum classification error rate compared to existing methods, such as diabetic retinopathy identification utilizing prognosis of micro aneurysm and early diagnosis for non-proliferative diabetic retinopathy depending on deep learning approaches (DRD-CNN-NPDR), a magnified adaptive feature pyramid network for automatic micro aneurysms identification (MAFPN-AMD-MAFP-Net) respectively. RESEARCH HIGHLIGHTS: Micro aneurysm detection using residual-based temporal attention Convolutional Neural Network (CNN) is proposed. To get rid of the retina background, guided box filtering is applied. COAT is used for segmenting the images into smaller parts RTCNN is used for accurate micro aneurysms disease classification. RT-CNN algorithm successfully identifies the micro aneurysms using EOA.

A Deep Learning-based Transfer Learning Approach Fine-Tuned for Detecting Diabetic Retinopathy

Diabetics have blood sugar levels that are abnormal. Because of this, their eyesight can be affected. Untreated diabetes has the potential to result in permanent blindness, a grave complication. Among the severe consequences, early detection stands out as a critical hurdle for ensuring effective treatment. Regrettably, accurately assessing fundus images relies on human expertise to determine the stage of diabetic retinopathy. A simplified detection process could greatly benefit a vast population. Leveraging convolutional neural networks (CNN), which have demonstrated efficacy across various domains, diabetic retinopathy identification has seen advancements. The possibility of preventing complete, irreversible blindness lies in timely identification, underscoring the need for a dependable screening mechanism. To enhance the efficiency of screening and reduce errors that plague current models, a novel system has been introduced, capable of categorizing distinct diabetic retinopathy stages. This study focuses on examining datasets of Indian diabetic retinopathy images, encompassing four classes: Microaneurysms (MA), Soft Exudates (SE), Hard Exudates (EX), and Hemorrhages (HE). By employing the proposed VGG16 architecture for feature extraction and the Logistic Regression classifier for classification, an impressive accuracy of 90.4% was achieved.

Precise lesion analysis to detect diabetic retinopathy using Generative Adversarial Network(GAN) and Mask-RCNN

Modern Medical diagnosis has a significant reliance on systematic image analysis for rational prognosis. With the advancement of computer vision methods, supported by deep learning algorithms, the non-invasive and early-stage detection of various diseases is a significant area of research. Amongst various image-based disease identification scopes, in this paper it is focused on Diabetic Retinopathy (DR), which is an eye disease caused by diabetes that can even lead to blindness. Therefore, early detection is critical to prevent visual disturbances from such diseases. In this study, the objective is to identify diabetic retinopathy by analysing fundus images using deep learning methods. Identifying diabetic retinopathy from fundus images, requires considerable improvement of image quality. In this work the retina images obtained by the fundus camera is improved by Generative Adversarial Network(GAN) for specific analysis. This study aimed to detect lesions using mask-RCNN techniques, where pretrained models such as R-50, R-101, and X-101 are utilized for robust segmentation and masking of the damaged regions of the fundus images. As the lesions are pathologically classified into two categories: ’exudates’ and ’microaneurysms’, similarly the input images are also mapped into two categories. Evaluation is based on the mean average precision (MAP) and the findings infer the conclusion. The comprehensive set of real-life retina images obtained from the clinical dataset benchmarked with the proposed mechanism and it is found that the proper segmentation and improvisation of fundus images by applying GAN have considerably enhanced the degree of accuracy in the Region-of-interest(ROI) bounding box for exact identification of Diabetic Retinopathy, along with reporting on stage of the disease. The aim of the work is well established in implementing the algorithm and finding the result for mapping it into the automated classification of fundus images in batches where the X-101 model seen to perform better in terms of finding Bounding Box Average Precision for both exudates (75.20%) and microaneurysms (67.202%) followed by Segmented Region Average Precision for exudates (62.363%) and microaneurysms (57.690%). The obtained results may reduce human intervention and classify a large number of input images in field-level examination in a faster manner.

Implementation of Image Enhancement and Edge Detection Algorithm on Diabetic Retinopathy (DR) Image Using FPGA

Diabetic retinopathy (DR) is an ocular ailment that may lead to loss of vision and eventual blindness among individuals diagnosed with diabetes. The blood vessels of the retina, a layer of light-sensitive tissue located at the posterior aspect of the ocular globe, are adversely impacted. The identification of DR entails the utilization of retinal fundus images. The detection of any form of abnormality in the eye through raw fundus images poses a significant challenge for medical practitioners. Hence, it is imperative to engage in the processing of fundus images. This paper delineates several image processing techniques for DR images, including but not limited to, manipulation of brightness levels, application of negative transformation, and utilization of threshold operations. It focuses on elucidating the enhancement techniques that pertain to DR images, which aim to optimize the visual quality of said images in order to facilitate more facile disease detection. The process of detecting edges within DR images is also executed by Sobel edge detection algorithm. In order to successfully execute the aforementioned algorithms, expedient and contemporaneous systems are favored to account for the intricacies of the image processing calculations. The exclusive utilization of software techniques in order to fulfill the prerequisites of advanced algorithms presents a significant challenge, owing to the multifarious processes that are involved in their computation, coupled with an exigent requirement for high processing speeds. The proposed model is utilized to articulate a proficient model for the design and execution of field programable gate array (FPGA)-based image enhancement processes along with the Sobel edge detection algorithm upon DR images. Finally, a Internet Protocol chip is developed that can combine multiple image enhancement operations into a single framework with less complexity.

Retracted: Identification of Diabetic Retinopathy through Machine Learning

Retracted: Identification of Diabetic Retinopathy through Machine Learning

Diabetic Retinopathy Severity Identification Using 3D Dual-Domain Attention Approach

The diabetic retinopathy (DR) is one of prominent reason of visual impairment among the people around the globe suffers from diabetes. Early and timely diagnosis of this problem can minimise the risk of proliferated diabetic retinopathy. Diabetes is caused by persistently high blood glucose levels, which leads to blood vessel aggravations and vision loss. Therefore, it becomes important to classify DR stages. An automated system for this purpose contains several phases like identification and classification of DR stages in fundus images. Deep learning techniques based on extraction of features and automatic extraction of features with a hybrid network have been presented for diabetic retinopathy detection. This method effectively identify diabetic retinopathy identification from the chest region by using the 3D Dual-Domain Attention Approach. The dual-domain attention module propised learns local and global information in spatial and context domains from encoding feature maps in Unet. Our attention module generates refined feature maps from the enlarged reception field at every stage by attention mechanisms and residual learning to focus on complex tumor regions. Experimental results show that the proposed network can identify the DR stages with high accuracy. The proposed method attains an F1-score of 91.34%, precision of 92.34%, accuracy of 98.65%, on the healthy retina, stage 1, stage 2, and stage 3 fundus images. Compared with other models, our proposed network achieves comparable performance.

Diabetic retinopathy stage detection using convolutional fine-tuned transfer Learning model

Diabetic Retinopathy (DR) is a prevalent eye condition that occurs as a frequent complication among individuals with diabetes, particularly those who have been living with the disease for an extended period of time. This study uses fundus images to diagnose DR at five stages from early to late with No DR, Mild, Moderate, Severe, and Proliferative DR, commonly known as Stage 0 to Stage 4, respectively. This will aid in the timely treatment of diabetic patients preventing them from developing DR as early as possible. We used two most popular open-source datasets, the DR Detection database, namely APTOS 2019 and EyePACS, and combined them to create a larger dataset to trade off the data-centric obstacle and shortfall for any Deep Learning-based prediction models. Data augmentation and preprocessing techniques are applied to the images before feeding them to the proposed model to get a more accurate and efficient one. In the modern age oriented to Artificial Intelligence (AI), it is necessary to thoroughly analyze the identification of DR based on the existing Deep Learning (DL) models. After learning about the limitations of existing models, we have fine-tuned the ResNet50, DenseNet201 and InceptionV3 to enhance the model performance of the detection and categorization of DR. We have since proposed three Deep Convolutional Neural Networks (DCNN) models with better outcome based on accuracy than the existing state-of-the-art (SOTA) models. The fine-tuned DenseNet201 model, among the other two, performed significantly better with a validation accuracy of 90.04% and a negligible amount of loss, irrespective of each class, under the best configurable test conditions.

SEGAN-BASED LESION SEGMENTATION AND OPTIMIZED RideNN FOR DIABETIC RETINOPATHY CLASSIFICATION

The most significant issue with diabetes is diabetic retinopathy (DR), which is the primary cause of blindness. DR typically develops no symptoms at the beginning of the disease, thus numerous physical examinations, including pupil dilation and a visual activity test, are necessary for DR identification. Due to the differences and challenges of DR, it is more challenging to identify it during the manual assessment. For DR patients, visual loss is prevented thanks to early detection and accurate therapy. Therefore, it is even more necessary to classify the severity levels of DR in order to provide a successful course of treatment. This study develops a deep learning method based on chronological rider sea lion optimization (CRSLO) for the classification of DR. The segmentation process divides the image into multiple subgroups, which is necessary for the appropriate detection and classification procedure. For the efficient identification of DR and classification of DR severity, the deep learning approach is used. Additionally, the CRSLO scheme is used to train the deep learning technique to achieve higher performance. With respect to testing accuracy, sensitivity, and specificity of 0.9218, 0.9304 and 0.9154, the newly introduced CRSLO-based deep learning approach outperformed other existing DR classification techniques like convolutional neural networks (CNNs), deep convolutional neural network (DCNN), synergic deep learning (SDL), HPTI-V4 and DR[Formula: see text]GRADUATE. The Speech Enhancement Generative Adversarial Network (SEGAN) model in use also produced increased segmentation accuracy of 0.90300.

Software programmes employed as medical devices

The opportunity to successfully develop and roll out patient-centric digital health platforms is represented by Software as a medical device (SaMD).Software is now a crucial component of all products and is widely integrated into digital platforms that are used for medical purposes as technology in all areas of health care continues to progress. Artificial intelligence (AI) is a potent and currently evolving technology that has the potential to enhance capabilities in a wide range of sectors. Medical device companies have been fascinated in artificial intelligence. Nowadays Artificial intelligence based medical devices are gaining a lot of attention. Artificial intelligence-enabled medical devices have the potential to completely transform the way that healthcare is provided by enabling physicians to diagnose and treat their patients more precisely and successfully while also enhancing their overall level of care. The three primary objectives of the medical devices that are incorporated with AI are being developed by medical device companies as technology progresses are chronic disease management, medical imaging and Internet of Things (IoT). Quite apart from its advantages, artificial intelligence in medical devices also has drawbacks, such as the necessity for regulation to keep up with the rate of technological innovation. The softwares such as Eye Art and IDxDR for the identification of diabetic retinopathy, Quant X for breast abnormalities, Gleamer BoneView for fractures on X rays and software for the management of type 1 diabetes i.e., Dreamed Advisor Pro and as well as the AI-ECG platform are just a few instances of the artificial intelligence-infused software’s that are discussed in this article.

Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging.

The severity of diabetic retinopathy (DR) is directly correlated to changes in both the oxygen utilization rate of retinal tissue as well as the blood oxygen saturation of both arteries and veins. Therefore, the current stage of DR in a patient can be identified by analyzing the oxygen content in blood vessels through fundus images. This enables medical professionals to make accurate and prompt judgments regarding the patient's condition. However, in order to use this method to implement supplementary medical treatment, blood vessels under fundus images need to be determined first, and arteries and veins then need to be differentiated from one another. Therefore, the entire study was split into three sections. After first removing the background from the fundus images using image processing, the blood vessels in the images were then separated from the background. Second, the method of hyperspectral imaging (HSI) was utilized in order to construct the spectral data. The HSI algorithm was utilized in order to perform analysis and simulations on the overall reflection spectrum of the retinal image. Thirdly, principal component analysis (PCA) was performed in order to both simplify the data and acquire the major principal components score plot for retinopathy in arteries and veins at all stages. In the final step, arteries and veins in the original fundus images were separated using the principal components score plots for each stage. As retinopathy progresses, the difference in reflectance between the arteries and veins gradually decreases. This results in a more difficult differentiation of PCA results in later stages, along with decreased precision and sensitivity. As a consequence of this, the precision and sensitivity of the HSI method in DR patients who are in the normal stage and those who are in the proliferative DR (PDR) stage are the highest and lowest, respectively. On the other hand, the indicator values are comparable between the background DR (BDR) and pre-proliferative DR (PPDR) stages due to the fact that both stages exhibit comparable clinical-pathological severity characteristics. The results indicate that the sensitivity values of arteries are 82.4%, 77.5%, 78.1%, and 72.9% in the normal, BDR, PPDR, and PDR, while for veins, these values are 88.5%, 85.4%, 81.4%, and 75.1% in the normal, BDR, PPDR, and PDR, respectively.