Retinal Scanning Research Articles

Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade

AbstractCardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for the analysis of different CVD‐related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with noninvasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI‐assisted early detection and prediction of cardiovascular diseases on a large scale. A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and AI. The study included 87 English‐language publications selected for relevance, and additional references were considered. This article provides an overview of the recent developments and difficulties in using AI and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and DL are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence

A comparative evaluation of deep learning approaches for ophthalmology

There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This allows ophthalmic researchers and practitioners to independently perform various deep-learning tasks. With the advancement in artificial intelligence (AI) and in the field of imaging, the choice of the most appropriate AI architecture for different tasks will vary greatly. The best-performing AI-dataset combination will depend on the specific problem that needs to be solved and the type of data available. The article discusses different machine learning models and deep learning architectures currently used for various ophthalmic imaging modalities and for different machine learning tasks. It also proposes the most appropriate models based on accuracy and other important factors such as training time, the ability to deploy the model on clinical devices/smartphones, heatmaps that enhance the self-explanatory nature of classification decisions, and the ability to train/adapt on small image datasets to determine if further data collection is worthwhile. The article extensively reviews the existing state-of-the-art AI methods focused on useful machine-learning applications for ophthalmology. It estimates their performance and viability through training and evaluating architectures with different public and private image datasets of different modalities, such as full-color retinal images, OCT images, and 3D OCT scans. The article is expected to benefit the readers by enriching their knowledge of artificial intelligence applied to ophthalmology.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans.

There is a growing interest in using computer-assisted models for the detection of macular conditions using optical coherence tomography (OCT) data. As the quantity of clinical scan data of specific conditions is limited, these models are typically developed by fine-tuning a generalized network to classify specific macular conditions of interest. Full thickness macular holes (FTMH) present a condition requiring urgent surgical repair to prevent vision loss. Other works on automated FTMH classification have tended to use supervised ImageNet pre-trained networks with good results but leave room for improvement. In this paper, we develop a model for FTMH classification using OCT B-scans around the central foveal region to pre-train a naïve network using contrastive self-supervised learning. We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, 51 FTMH+ eyes, 3 B-scans from each eye). On three replicate data splits, 3D spatial contrast pre-training yields a model with an average F1-score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared to an average F1-score of 0.831 for FTMH detection by ImageNet pre-trained models. These results demonstrate that even limited data may be applied toward self-supervised pre-training to substantially improve performance for FTMH classification, indicating applicability toward other OCT-based problems.

Ensemble Diabetic Retinopathy Detection in 2-D Color Fundus Retina Scan

Ensemble Diabetic Retinopathy Detection in 2-D Color Fundus Retina Scan

Design of Multidomain Feature Analysis Model for Estimation of Anemic Conditions via Deep Transfer Learning

The detection of anemia from anemic retinopathy involves the identification and analysis of specific retinal changes that occur as a result of severe anemia which requires analysis of multimodal sources. Existing models for identification of these conditions either showcase high complexity, or have lower efficiency when evaluated on clinical scans. Moreover, most of these models cannot be scaled for multi-disease images, which limits their applicability under real-time use cases. To overcome these issues, this research article proposes the design of an efficient and novel multidomain feature analysis model for the estimation of anemic conditions with the help of deep transfer learning process. The proposed model initially represents retinal image scans into multiple domains via estimation of Frequency, Gabor, Convolution, Wavelet and Entropy features. These features are selected via Bacterial Foraging Optimizer (BFO), which assists in retaining high variance feature sets. Due to this, the model is able to reduce redundancies, that assists in improving the speed and accuracy of classification operations. To perform this task, the model uses a set of customized 1-D Cascaded Binary CNN, that assist in categorizing input images into 4 different classes. These include, “Anemic due to Glaucoma”, “Glaucoma”, “Anemic”, and “Normal”, with 98.3% accuracy which is 4.5% higher than existing methods. The model is also able to improve the precision by 2.9%, recall by 3.5%, while reducing the delay needed for classification by 5.9% when compared with existing techniques. Due to which, the model is highly useful for clinical deployments.

Repeatability of retinal and choroidal optical coherence tomography angiography indices in healthy children and young adults.

To evaluate the repeatability of retinal and choroidal optical coherence tomography angiography (OCT-A) indices among healthy children and compare it to healthy young adults. This prospective study captured 3 mm × 3 mm and 6 mm × 6 mm macular OCT-A scans including superficial and deep retinal layers, choriocapillaris and deep choroid over two visits, 1 week apart at approximately the same time of day, for 22 healthy adults (18-30 years) and 21 children (6-15 years). Magnification and projection-artefact corrected indices extracted using a custom image analysis program and individual biometry were compared between visits using Bland-Altman analysis and intraclass correlation (ICC). Retinal indices included foveal avascular zone metrics, perfusion and vessel density and choroidal indices included choriocapillaris flow deficit metrics and deep choroid perfusion density, in the foveal, parafoveal and perifoveal regions. Repeatability between adults and children was compared with F-test. Bland-Altman analysis showed that the mean differences between repeated OCT-A indices were not significantly different from zero for either of the zones, layers and scan sizes in the two age groups (p > 0.05) except for foveal vessel density and foveal avascular zone perimeter (p = 0.04 for both) of 6-mm-deep retinal layer scans. The ICC ranged between 0.67 and 0.99. Significantly higher variability between visits (p < 0.05) in the indices was noted among adults than children, especially for choroidal indices of larger scan size. The retinal and choroidal OCT-A indices in the foveal, parafoveal and perifoveal zones were repeatable in healthy children except for the foveal vessel density and foveal avascular zone perimeter of the 6-mm-deep retinal layer, which exhibited statistically borderline differences between visits. The adult group showed more variability between visits compared to children, especially in the larger scan size for choroidal OCT-A indices.

Leveraging Face Recognition Technology for Secure ATM Transaction

ATM or Automated Teller Machines are widely used by people nowadays. Performing cash withdrawal transactions with ATMs is increasing day by day. ATMs are a very important device throughout the world. The existing conventional ATM is vulnerable to crimes because of the rapid technology development. A total of 270,000 reports have been reported regarding debit card fraud and this was the most reported form of identity theft in 2021. A secure and efficient ATM is needed to increase the overall experience, usability, and convenience of the transaction at the ATM. In today's world, the area of computer vision is advancing at a breakneck pace. The recent progress in biometric identification techniques, including fingerprinting, retina scanning, and facial recognition has made a great effort to rescue the unsafe situation at the ATM. Specifically, the goal of this project is to give a computer vision method to solve the security risk associated with accessing ATM machines. This project proposes an automatic teller machine security model that uses electronic facial recognition using Deep Convolutional Neural Network. If this technology becomes widely used, faces would be protected as well as their accounts. Face Verification Clickbait Link will be generated and sent to bank account holders to verify the identity of unauthorized users through some dedicated artificial intelligent agents, for remote certification. However, it is obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of account safety making it possible for the actual account owner alone to have access to his accounts. This eliminates the possibility of fraud resulting from ATM card theft and copying. The experimental results on real-time datasets demonstrate the superior performance of the proposed approach over state-of-the-art deep learning techniques in terms of both learning efficiency and matching accuracy

Anomaly Detection and Biomarkers Localization in Retinal Images.

Background: To design a novel anomaly detection and localization approach using artificial intelligence methods using optical coherence tomography (OCT) scans for retinal diseases. Methods: High-resolution OCT scans from the publicly available Kaggle dataset and a local dataset were used by four state-of-the-art self-supervised frameworks. The backbone model of all the frameworks was a pre-trained convolutional neural network (CNN), which enabled the extraction of meaningful features from OCT images. Anomalous images included choroidal neovascularization (CNV), diabetic macular edema (DME), and the presence of drusen. Anomaly detectors were evaluated by commonly accepted performance metrics, including area under the receiver operating characteristic curve, F1 score, and accuracy. Results: A total of 25,315 high-resolution retinal OCT slabs were used for training. Test and validation sets consisted of 968 and 4000 slabs, respectively. The best performing across all anomaly detectors had an area under the receiver operating characteristic of 0.99. All frameworks were shown to achieve high performance and generalize well for the different retinal diseases. Heat maps were generated to visualize the quality of the frameworks' ability to localize anomalous areas of the image. Conclusions: This study shows that with the use of pre-trained feature extractors, the frameworks tested can generalize to the domain of retinal OCT scans and achieve high image-level ROC-AUC scores. The localization results of these frameworks are promising and successfully capture areas that indicate the presence of retinal pathology. Moreover, such frameworks have the potential to uncover new biomarkers that are difficult for the human eye to detect. Frameworks for anomaly detection and localization can potentially be integrated into clinical decision support and automatic screening systems that will aid ophthalmologists in patient diagnosis, follow-up, and treatment design. This work establishes a solid basis for further development of automated anomaly detection frameworks for clinical use.

Biomarkers and Cognition Study, Singapore (BIOCIS): Protocol, Study Design, and Preliminary Findings

BackgroundThe focus of medicine is shifting from treatment to preventive care. The expression of biomarkers of dementia and Alzheimer’s disease (AD) appear decades before the onset of observable symptoms, and evidence has emerged supporting pharmacological and non-pharmacological interventions to treat modifiable risk factors of dementia. However, there is limited research on the epidemiology, clinical phenotypes, and underlying pathobiology of cognitive diseases in Asian populations.ObjectivesThe objectives of the Biomarkers and Cognition Study, Singapore(BIOCIS) are to characterize the underlying pathobiology of Cognitive Impairment through a longitudinal study incorporating fluid biomarker profiles, neuroimaging, neuropsychological and clinical outcomes in a multi-ethnic Southeast Asian population.Design, Setting, ParticipantsBIOCIS is a 5-year longitudinal study where participants are assessed annually. 2500 participants aged 30 to 95 will be recruited from the community in Singapore. To investigate how pathology presents with or without minimal clinical symptoms and vice versa, CI and unimpaired individuals will be recruited. Participants will undergo assessments to characterise biomarkers of dementia through neuroimaging, fluid biomarkers, cognitive assessments, behavioural and lifestyle profiles, retinal scans and microbiome indicators.ResultsSince commencement of recruitment in February 2022, 1148 participants have been enrolled, comprising 1012 Chinese, 62 Indian, and 35 Malay individuals. Mean age and education is 61.32 years and 14.34 years respectively with 39.8% males. 47.9 % of the cohort are employed and 32.06% have a family history of dementia. The prevalence of cerebral small vessel disease is 90.2% with a mean modified Fazekas white matter hyperintensity score of 4.1.ConclusionThe BIOCIS cohort will help identify novel biomarkers, pathological trajectories, epidemiology of dementia, and reversible risk factors in a Southeast Asian population. Completion of BIOCIS longitudinal data could provide insights into risk-stratification of Asians populations, and potentially inform public healthcare and precision medicine for better patient outcomes in the prevention of Alzheimer’s disease and dementia.

Sensitivity and specificity of SS-OCT for detecting macular pathologies vs SD-OCT.

To evaluate the sensitivity and specificity of swept-source optical coherence tomography (SS-OCT) biometer compared with the gold standard spectral-domain optical coherence tomography (SD-OCT) for detecting macular pathology in patients with cataract. Eye Centers of Tennessee, Crossville, TN. Prospective, cross-sectional, observational, examiner-masked. The study included 132 participants aged 50 years and older, who underwent precataract surgery work-up. All participants underwent fixation check retinal scans using SS-OCT biometer (IOLMaster 700) as well as full macular scans using Cirrus SD-OCT. 3 independent masked examiners evaluated the scans if they were normal or had a suspected pathology. Different measures of diagnostic accuracy were calculated for 3 examiners. True positive rate (sensitivity) ranged from 71.1% (32/45) to 79.2% (42/53), and false negative rate was between 20.8% (11/53) and 28.9% (13/45) for the 3 examiners. True negative rate (specificity) ranged from 86.8% (59/68) to 94.1% (64/68), and false positive rate was between 5.9 (4/68) and 13.2% (9/68). The fitted receiver operating characteristic area ranged from 0.83 to 0.95. Using retinal SS-OCT biometer scans as a replacement of the dedicated macular SD-OCT for screening or diagnosing macular health would not be appropriate because of its low sensitivity. SS-OCT biometer may potentially fail to identify approximately one-fourth of patients who actually have the disease. Therefore, the final decision on macular health should be based on the gold standard SD-OCT scans. When full macular SD-OCT scans are not accessible, the limited retinal scan information from SS-OCT biometer may still provide useful insights into the macular health.

The eye in forensic practice: In the living.

Eye examination plays an important role when living individuals are forensically investigated. The iris colour, retinal scans and other biometric features may be used for identification purposes while visual impairments may have legal implications in employment, driving and accidents. Ocular manifestations provide clues regarding substance abuse, poisoning and toxicity, and evidence of trauma, abuse or disease can be revealed along with psychological traits and lifestyle. Thus, the eye is a valuable tool in forensic investigations of living subjects, providing identifying characteristics along with health information. This review focuses on the medico-legal aspects of the eye's contribution when the living are subjected to forensic examination.

Glaucoma Detection Using Explainable AI and Deep Learning

INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed.&#x0D; OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection.&#x0D; RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS).&#x0D; CONCLUSION: This article proposes and compares the performance metrics of ANFIS &amp; SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

CLINICALLY RELEVANT POSTERIOR VITREOUS DETACHMENT STAGING USING CIRCUMPAPILLARY AND MACULAR VOLUME OPTICAL COHERENCE TOMOGRAPHY.

This study was designed to investigate retinal nerve fiber layer circumpapillary optical coherence tomography to determine posterior vitreous detachment (PVD) status and to develop a clinically relevant PVD grading scale based on retinal nerve fiber layer circumpapillary optical coherence tomography to determine the incidence of PVD by age and association with vitreomacular traction disorders. Ophthalmic images and medical records of patients with retinal diseases were retrospectively analyzed by three masked graders using retinal nerve fiber layer circumpapillary optical coherence tomography and macular optical coherence tomography. Based on PVD status, eyes were categorized into five newly defined PVD stages. Among 2002 eyes, PVD stages were as follows: A) 25 (1.25%); B) 725 (36.21%); C-) 248 (12.39%); C+) 151 (7.54%); D) 851 (42.51%); X) 2 (0.1%). Posterior vitreous detachment was correlated with advanced age (P < 0.0001). Limited separation or partial separation between lamella within the posterior vitreous cortex (Stage B) was noted early (68% of eyes <18 years). Overall, 34% of eyes >70 years did not exhibit complete PVD. Of 75 eyes with tractional vitreoretinal disorders, 64 (85.3%) were Stage C-/C+, identifying Stage C as the high-risk "complication" stage. Imaging analyses using retinal nerve fiber layer circumpapillary optical coherence tomography and macular optical coherence tomography scans in conjunction allow rapid assessment of the PVD stage. These techniques can assist clinicians and surgeons in counseling patients and planning surgical approaches. Observations confirmed the progression of PVD through predictable stages and the progression of PVD with age.

Enhancing Readability and Detection of Age-Related Macular Degeneration Using Optical Coherence Tomography Imaging: An AI Approach.

Artificial intelligence has been used effectively in medical diagnosis. The objective of this project is to examine the application of a collective AI model using weighted fusion of predicted probabilities from different AI architectures to diagnose various retinal conditions based on optical coherence tomography (OCT). A publicly available Noor dataset, comprising 16,822, images from 554 retinal OCT scans of 441 patients, was used to predict a diverse spectrum of age-related macular degeneration (AMD) stages: normal, drusen, or choroidal neovascularization. These predictions were compared with predictions from ResNet, EfficientNet, and Attention models, respectively, using precision, recall, F1 score, and confusion matric and receiver operating characteristics curves. Our collective model demonstrated superior accuracy in classifying AMD compared to individual ResNet, EfficientNet, and Attention models, showcasing the effectiveness of using trainable weights in the ensemble fusion process, where these weights dynamically adapt during training rather than being fixed values. Specifically, our ensemble model achieved an accuracy of 91.88%, precision of 92.54%, recall of 92.01%, and F1 score of 92.03%, outperforming individual models. Our model also highlights the refinement process undertaken through a thorough examination of initially misclassified cases, leading to significant improvements in the model's accuracy rate to 97%. This study also underscores the potential of AI as a valuable tool in ophthalmology. The proposed ensemble model, combining different mechanisms highlights the benefits of model fusion for complex medical image analysis.

Compact snapshot hyperspectral camera for ophthalmology

Hyperspectral imaging is an emerging technique that allows measurement of spectral absorption at each point of a scene, thus offering the capability to identify and characterize important biomarkers for clinical practice and therapeutic research, as well as enhancing image identification of important structures. So far, few hyperspectral cameras have been used for retinal scanning because of the need to acquire the image in a fraction of a second. Here, and to the best of our knowledge, we present a novel concept of a snapshot hyperspectral camera suited for retinal imaging. We demonstrate the technique by presenting the optical density spectrum of a healthy patient’s retina in the 450–700 nm range, together with the spectral response of several retinal features.

Employing texture loss to denoise OCT images using generative adversarial networks.

OCT is a widely used clinical ophthalmic imaging technique, but the presence of speckle noise can obscure important pathological features and hinder accurate segmentation. This paper presents a novel method for denoising optical coherence tomography (OCT) images using a combination of texture loss and generative adversarial networks (GANs). Previous approaches have integrated deep learning techniques, starting with denoising Convolutional Neural Networks (CNNs) that employed pixel-wise losses. While effective in reducing noise, these methods often introduced a blurring effect in the denoised OCT images. To address this, perceptual losses were introduced, improving denoising performance and overall image quality. Building on these advancements, our research focuses on designing an image reconstruction GAN that generates OCT images with textural similarity to the gold standard, the averaged OCT image. We utilize the PatchGAN discriminator approach as a texture loss to enhance the quality of the reconstructed OCT images. We also compare the performance of UNet and ResNet as generators in the conditional GAN (cGAN) setting, as well as compare PatchGAN with the Wasserstein GAN. Using real clinical foveal-centered OCT retinal scans of children with normal vision, our experiments demonstrate that the combination of PatchGAN and UNet achieves superior performance (PSNR = 32.50) compared to recently proposed methods such as SiameseGAN (PSNR = 31.02). Qualitative experiments involving six masked clinical ophthalmologists also favor the reconstructed OCT images with PatchGAN texture loss. In summary, this paper introduces a novel method for denoising OCT images by incorporating texture loss within a GAN framework. The proposed approach outperforms existing methods and is well-received by clinical experts, offering promising advancements in OCT image reconstruction and facilitating accurate clinical interpretation.

Computational Model for the Detection of Diabetic Retinopathy in 2-D Color Fundus Retina Scan

Background:: Diabetic Retinopathy (DR) is a growing problem in Asian countries. DR accounts for 5% to 7% of all blindness in the entire area. In India, the record of DR-affected patients will reach around 79.4 million by 2030. Aims:: The main objective of the investigation is to utilize 2-D colored fundus retina scans to determine if an individual possesses DR or not. In this regard, Engineering-based techniques such as deep learning and neural networks play a methodical role in fighting against this fatal disease. Methods:: In this research work, a Computational Model for detecting DR using Convolutional Neural Network (DRCNN) is proposed. This method contrasts the fundus retina scans of the DR-afflicted eye with the usual human eyes. Using CNN and layers like Conv2D, Pooling, Dense, Flatten, and Dropout, the model aids in comprehending the scan's curve and color-based features. For training and error reduction, the Visual Geometry Group (VGG-16) model and Adaptive Moment Estimation Optimizer are utilized. Results:: The variations in a dataset like 50%, 60%, 70%, 80%, and 90% images are reserved for the training phase, and the rest images are reserved for the testing phase. In the proposed model, the VGG-16 model comprises 138M parameters. The accuracy is achieved maximum rate of 90% when the training dataset is reserved at 80%. The model was validated using other datasets. Conclusion:: The suggested contribution to research determines conclusively whether the provided OCT scan utilizes an effective method for detecting DRaffected individuals within just a few moments.

Beyond Vision: Potential Role of AI-enabled Ocular Scans in the Prediction of Aging and Systemic Disorders

In all medical subfields, including ophthalmology, the development of artificial intelligence (AI), particularly cutting-edge deep learning frameworks, has sparked a quiet revolution. The eyes and the rest of the body are anatomically related because of the unique microvascular and neuronal structures they possess. Therefore, ocular image-based AI technology may be a helpful substitute or extra screening method for systemic disorders, particularly in areas with limited resources. This paper provides an overview of existing AI applications for the prediction of systemic diseases from multimodal ocular pictures, including retinal diseases, neurological diseases, anemia, chronic kidney disease, autoimmune diseases, sleep disorders, cardiovascular diseases, and various others. It also covers the process of aging and its predictive biomarkers obtained from AI-based retinal scans. Finally, we also go through these applications existing problems and potential future paths.

BLYNK RFID and Retinal Lock Access System

The BLYNK RFID AND RETINAL LOCKACCESS SYSTEM describes a digital door lock system that uses an ESP32-CAM module, which is a budget friendly development board with a very small size camera and a micro-SD card slot. The system uses retinal recognition technology to detect the retinal of the person who wants to access the door. The AI-Thinker ESP32-CAM module takes pictures of the person and sends them to the owner via the BLYNK application installed on their mobile phone. The owner can then grant permission to access the door based on the person’s identity. When deploying your BLYNK RFID and retinal scanner project, it's important to consider scalability and maintenance. As your user base and access requirements may change over time, plan for future expansion and updates. Regularly review and update your system's firmware, libraries, and security measures to stay ahead of potential vulnerabilities and evolving best practices in access control. Monitoring and auditing your system's usage is crucial. The Blynk platform can help you gather data on access attempts and system performance, allowing you to analyze the data for any anomalies and potential security breaches. This data can be valuable for compliance, troubleshooting, and performance optimization. Key Word: retinal and RFID scanning for lock to authentic users, using an ESP32-CAM and RFID reader controlling through BLYNK.