Description:Field of Invention
The technology of information it appears that you have an interest in gaining additional knowledge on the application of deep neural networks (DNN) for the disease diagnosis of retinal conditions. It would appear that the Innovative Eye Deep-Net is a sophisticated artificial intelligence model that has been developed to accurately diagnose a variety of retinal diseases.
Objectives of the Invention
The Operation of Eye Deep-Net: There is a high probability that the Eye Deep-Net model is trained using extensive datasets of labelled retinal images. It does this by employing convolutional neural networks, often known as CNNs, in order to extract patterns, identify anomalies, and categorize various skin conditions. Some of the benefits of using this AI-based strategy are as follows:
Perspectives on the Effects of Eye Disorders on Mental and Social Health: Not only do eye problems affect one's ability to see, but they also have an impact on one's self-esteem, confidence, and productivity. It is possible for children who have apparent eye disorders to be bullied or to be socially isolated, while adults may have difficulty doing daily activities and having difficulty working efficiently. Preventing these problems and improving overall quality of life can be accomplished by early detection and treatment using solutions powered by artificial intelligence.
Recent technological developments have brought about significant advantages in virtually every aspect of life, particularly in the sphere of medicine. It has been proposed that a number of different strategies and models can be utilized to enhance the efficiency and quality of medical solutions. Because of the advancements that have been made in automatic disease detection, the social health system has proven to have undergone a substantial improvement. Furthermore, an application for attention-deficit/hyperactivity disorder (ADD), specifically retinal symptom analysis, offers a one-of-a-kind possibility to improve.
Background of the Invention
This study has implications for the domains of ophthalmology and medical image processing, as well as for the implementation of artificial intelligence (AI) in the medical field. Deep learning is being utilized with the intention of automating the identification of retinal disorders, which is a key stage in the process of preventive diagnosis and treatment planning. Particular focus is being dedicated to the employment of deep learning specifically for this purpose.
Diseases of the retina, especially those induced by systemic diseases like diabetes, ageing, and genetics, are the main reasons for blindness worldwide. This is particularly the case for retinal diseases that are presented in (US20200312038A1). One of the most prevalent signs of diabetes is an instance known as diabetic retinopathy (DR), which is characterized by destruction of blood vessels associated with the retina. It is one of the most prevalent symptoms of diabetes. Diabetic retinopathy (DR) may result in vision loss as well as blindness if not treated. In (DE102019/201988A1), Glaucoma, due to an increase in intraocular pressure leading to damage to the optic nerve, is yet another acute condition that impacts the retina.
Glaucoma results from the increasing pressure within the eye. Cataracts is a condition where the lens of the eye becomes cloudy, leading to the blurring of vision. The condition referred to as macular degeneration is one in which there is a slow breakdown of the macula, and this affects the central vision negatively. Uveitis is a condition that is marked by inflammation of the uvea, and it is in a position to result in a progressive loss of vision. The early diagnosis of these diseases is extremely important so that irreversible loss of vision is prevented. Ophthalmologists were earlier tasked with the visual screening of retinal photographs in an attempt to reach a positive diagnosis.
The process is susceptible to errors introduced by human intervention, and it may take longer to perform compared to other methods. Automated systems that are based on deep learning can significantly improve diagnostic accuracy, ease the burden on healthcare providers, and make healthcare solutions that are not only more accessible but also more speedily deployed. This is particularly true in rural areas that it may be quite challenging to reach specialists, as discussed in (US20230200676A1). For the identification of multiple eye diseases from medical retinal images, the goal of this study is to build a model which is powered by artificial intelligence and utilizes ResNet50 and VGG16. With the assistance of this innovation, ophthalmologists will be facilitated to receive assistance in early detection of disease as well as treatment planning, respectively. The goal of the innovation is to expand the precision of diagnosis, automate disease detection, and implement the system in clinical practice is discussed in (US10722180B2).
Deep learning models, specifically convolutional neural networks (CNNs), are applied extensively in medical imaging in (US11723579B2). This is because these models can learn automatically from images. The data collection process entails gathering huge datasets of patients' retinal images. Such images comprise fundus images as well as Optical Coherence Tomography (OCT) scans.
Preprocessing process entails the augmentation, noise removal, and normalization of images to enhance the model's accuracy. Model Training: The CNN-based model is trained with the help of labelled photos, and it learns to recognize patterns that are related with particular eye disorders. Feature Extraction: The model is able to identify significant characteristics such as abnormalities in blood vessels, lesions, changes in the macular structure, and damage to the optic nerve. Classification and Prediction: The AI system classifies the disease and provides a probability score for diagnosis from the features that were extracted about it. Diabetic retinopathy can be detected using the labeled images included in the DIARETDB1 database. Eyepatch, otherwise referred to as the Kaggle DR Dataset, is among the largest datasets for diabetic retinopathy grading. Labelled images of different retinal diseases are covered in the APTOS 2019 Blindness Detection feature. Labelled OCT scans for Alzheimer's and diabetic macular oedema (DME) are covered in the OCT2017 Dataset is demonstrated in (US20230048571A1).
Glaucoma diagnosis is the main target of the RIM-ONE system. Expert annotation and validation through collaborating with retina experts and ophthalmologists to carry out hand labelling. Crowdsourcing medical labelling services can be used to make annotation faster. Prolonged multiple expert checks to ensure high agreement among experts is discussed in (US20180104439A1). In (US10064559B2), Automated and AI-Aided Labelling - Weakly Supervised Learning - Pre-trained models used to aid in initial annotating - Artificial intelligence is also used in this method. Using unlabeled data to improve feature extraction is referred to as "self-supervised learning." Generative artificial intelligence (GANs and data augmentation) is that used to generate artificial data in a bid to enhance class balance. Standardization and benchmarking of practices in labeling retinal defects, the creation of universal annotation criteria is required.

Summary of the Invention
Our group has developed a novel visual-aided diagnosis algorithm that uses Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) to enhance medical diagnosis of retinal diseases. Based on the objective of delivering illness diagnosis with very high accuracy, reliability, and interpretability, this hybrid model leverages the advantages that are inherent in both traditional machine learning techniques and deep models.
Detailed Description of the invention
In the first step, which is referred to as picture preprocessing and feature extraction, the set of images that were produced by the use of fundus photography and Optical Coherence Tomography (OCT) is performed. The images were produced by merging the two methods mentioned above. The process starts with this step, which is the initial step. After this, the photos are used in the following procedures that are undertaken as a result of this. There are a wide variety of image enhancement methods that are used in attempts to gain an increased level of clarity in the enhanced photographs. The methods that come under this category are augmentation, segmentation, and reduction of noise. To achieve the improvement required, it is done in this way in an effort to meet the needs for the improvement desired. Outside of the retina, there are several significant elements that can be excised. These elements are the retina. These elements include lesions, blood vessels, the optic nerve head, the macular layer thickness, and several other significant properties. The macular layer thickness is also included within the retina, which is another significant element to consider. Deep Neural Networks, commonly referred to as DNNs, are a type of neural network that are utilized for the purpose of learning features. That they can learn new properties is where the name comes from. The computer-aided automatic extraction of high-level spatial and structural information from retinal images is an accomplishment that can be achieved through the use of a Convolutional Neural Network, also simply known as a CNN.
This objective can be attained. There is no chance that this goal can be achieved without a single human being involved in any manner that is even plausible. The capability of deep learning models to learn patterns that are linked with some eye conditions is a major advantage that these models provide. This is a genuinely critical breakthrough. Cataracts, macular degeneration caused by age (AMD), glaucoma, and diabetes retinopathy are some of the conditions that fall under this category of eye diseases. Learning is the process that eventually leads to acquiring these patterns, and it is the start of the process. This type of learning is known as human learning. The term SVM stands for a support vector machine, which is used for classification. There exists a Support Vector Machine. With regard to the process of training the support vector machine classifier, the method that is used is the use of the higher-level features that are constructed by the deep neural network. This is the method that is used. The application of statistical support vector machines, also more commonly known as SVMs, is an outcome of the fact that they are capable of handling small datasets efficiently and ensuring that proper categorization is achieved.
This is in addition to the fact that it helps in the classification of the numerous diseases that affect the retina. A greater accuracy along with the process that is being executed Support vector machine was able to enhance the accuracy of the process of classification, even though the deep neural network was successful in extracting some properties from the data. Despite the fact that the deep neural network successfully managed to find success, this was the scenario that ensued. On of the most common scenarios that might be faced with deep learning models is overfitting. This is just one of many possible issues that could arise with these models. There are hundreds of other possible issues that might arise. The use of support vector machines, also as they are more often simply known, the SVM, is one of the means by which assistance in the alleviation of this issue can be made. This is one of the methods that can be considered.
This ultimately leads to an improvement in generalization, and it is this which is the final point of arrival. This is the inference which can be made from the general findings. Inference of meaning is an important benefit which accompanies increased ability. As far as traditional support vector machine models are concerned, it is desirable for medical professionals to have decision boundaries which are defined in a way which is explicit and intuitive. Because of this, it is much simpler to explain the diagnostic, which ultimately leads to the conclusion. This is the rationale for this. One of the most important factors that plays a role in determining whether or not deep learning models are successful in recognizing retinal illnesses is the availability of datasets that have been labeled and are of a high quality. This is one of the most essential attributes that considers. This is because the availability of these data sets is of unparalleled importance. Considering this specific characteristic, which is among the most important ones, is something that needs to be done. This is due to the fact that one of the variables contributing to the issue is the accessibility of datasets of this nature. This is why this is so. It is important for doctors, researchers in artificial intelligence, and data scientists to collaborate in a bid to effectively accomplish the process of collecting and categorizing retinal images, a task that not only requires importance but also difficulty. In an effort to achieve this, it is imperative for them to work together. So that they can succeed in achieving this goal, they will have to cooperate with each other.
Brief description of Drawing
In the figure which are illustrate exemplary embodiments of the invention.
Fig. 1 Predict disease progression and identify patients at high risk of vision loss
Fig. 2 Transfer learning (Tl) for eye disease classification , Claims:Claim:
1. A system/method for automated multi-class retinal disease diagnosis using deep neural networks, said system/method comprising the steps of:
a. The system aims to overcome difficulties related to achieving high sensitivity and precision in automated retinal disease diagnosis by implementing deep learning algorithms specifically for classification of multi-class Dry Eye Disease (DED).
b. The system addresses challenges associated with small and imbalanced datasets that have hindered previous research effectiveness. The existing deep learning models developed for general image classification (e.g., ImageNet architectures) are unable to capture subtle, clinically significant features in fundus images, resulting in reduced diagnostic accuracy; the system overcomes this limitation.
d. The system optimizes convolutional neural network (CNN) architectures to improve classification of diabetic eye disease and other retinal disorders, aiming to meet or exceed performance standards set by the Black and White Association (BDA). The system enhances sensitivity in detecting early-stage and sight-threatening DED by fine-tuning deep learning models and employing advanced feature extraction techniques.
f. The potential future improvements include hybrid artificial intelligence models, attention mechanisms, and domain-specific pretraining to further enhance accuracy and practical applicability in retinal disease diagnosis.
g. The system develops an Innovative Eye Deep-Net, a specialized deep neural network-based artificial intelligence model designed for automated diagnosis of multiple classes of retinal diseases. The system provides a scalable and reliable solution for early detection of retinal diseases, improving patient outcomes and transforming clinical diagnostics.
2. As mentioned in Claim 1, the system implements deep learning algorithms that effectively handle small and imbalanced retinal image datasets, improving diagnostic sensitivity and precision. The system optimizes CNN architectures to enhance classification accuracy of diabetic eye disease and other retinal disorders, complying with or exceeding BDA standards.
3. According to Claim 1, the system fine-tunes deep learning models and utilizes sophisticated feature extraction techniques to increase sensitivity for early-stage and sight-threatening retinal disease detection. The system incorporates potential future advancements such as hybrid AI models, attention mechanisms, and domain-specific pretraining to further improve diagnostic performance.
4. According to Claim 1, the system comprises the Innovative Eye Deep-Net, a deep neural network-based AI model specialized for automated multi-class retinal disease diagnosis. The implementation of this system enables scalable, reliable, and early detection of retinal diseases, thus improving patient care and clinical outcomes.