Description:FIELD OF THE INVENTION
This invention relates to healthcare systems, and more particularly to an AI-driven system for predicting and managing eye health risks in diabetic patients.
BACKGROUND OF THE INVENTION
Diabetic patients face an increased risk of vision problems such as diabetic retinopathy due to fluctuations in blood sugar and lifestyle factors. Regular assessment of eye health, blood sugar, diet and physical activity is essential to prevent complications. This AI system predicts eye health risks and provides early warnings empowering patients to take timely actions for vision preservation.
The existing approach for managing diabetic eye health focus on regular eye exams and blood sugar monitoring and limited to lifestyle factors such as diet and physical exercise. This gap limits the potential for early detection and personalized care, increasing the risk of eye complications. The proposed AI-driven system to bridge this gap by integrating eye tests, diabetes records, physical activity, and dietary patten data to predict eye health risks. It offers early alerts and tailored recommendations to help prevent vision problems, adapting advice over time based on lifestyle changes. This comprehensive approach empowers diabetic patients to better protect their vision.
 
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention addresses the limitations of existing methods by providing an AI-Driven Diabetic Eye Health Risk Prediction and Management System. This innovative system integrates AI with healthcare data specific to diabetic patients to oversee and predict eye health risks. The system collects data from various sources, including eye check-up details, blood sugar patterns, physical activity levels, and dietary information.
The AI analyzes this data to provide clear insights, analyzing how blood sugar and lifestyle may impact vision over time. The system classifies patients as either at-risk or stable, advising those at risk to consult an eye specialist. Continuous health monitoring allows for regular updates and adjustments, creating a feedback loop that supports effective eye health management.
This approach provides diabetic patients with early warnings and customized recommendations to protect their vision. The system's ability to integrate diverse health metrics and provide personalized risk assessments represents a significant advancement in diabetic eye care.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This proposed system integrates AI driven with healthcare data specific to diabetic patient healthcare to oversee and predict eye health risks. It initiates the processes of collecting data from eye check-up details, blood sugar patterns, physical activity level and dietary information. The AI analyze this data to provide clear insights, analyzing how blood sugar and lifestyle may impact on vision over time. The system classified patients as either at-risk or stable, advising those at risk to see an eye specialist. Continuous health monitoring allows for regular updates and adjustments, creating a feedback loop that supports eye health management. This approach provides diabetic patients with early warnings and customized recommendations to protect their vision.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
 
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The AI-Driven Diabetic Eye Health Risk Prediction and Management System comprises several key components working in concert. Data collection is a crucial first step. The system gathers data from multiple sources. Eye check-up details, including visual acuity, intraocular pressure, and retinal examinations, are input into the system. Blood sugar patterns, obtained through self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM) devices, are regularly uploaded. Physical activity levels, tracked through wearable devices or self-reported activity logs, are also integrated. Dietary information, collected through food diaries or nutritional tracking apps, completes the dataset.
This data is then securely transmitted and stored within a cloud-based database. The core of the system is the AI engine, which utilizes machine learning algorithms trained on a large dataset of diabetic patient health information. These algorithms are designed to identify patterns and correlations between various health metrics and the development or progression of diabetic eye disease. The AI analyzes the collected data, considering the combined influence of factors like blood sugar control, physical activity, and diet on vision health. The AI outputs a risk score, classifying patients as either at-risk or stable. For at-risk patients, the system generates alerts and recommends consultation with an eye specialist. Personalized recommendations regarding lifestyle modifications, such as dietary changes and increased physical activity, are also provided.
The system incorporates a user interface, accessible through a mobile app or web portal, allowing patients to view their risk scores, access personalized recommendations, and track their progress over time. Healthcare providers can also access the system to review patient data and make informed treatment decisions. Continuous health monitoring is a key feature. The system regularly updates risk assessments based on new data inputs, creating a dynamic feedback loop. This allows for early detection of changes in eye health and timely intervention. The system is designed to comply with all relevant data privacy regulations, ensuring the security and confidentiality of patient information. The interaction of data collection, AI analysis, risk prediction, and personalized recommendations empowers diabetic patients to actively manage their eye health and reduce their risk of vision loss.
The system's modular design allows for integration with existing healthcare systems and electronic health records (EHRs). The AI algorithms are continuously refined and updated as new data becomes available, improving the accuracy and reliability of risk predictions. The system's proactive approach, combining continuous monitoring with personalized interventions, has the potential to significantly improve outcomes for diabetic patients and reduce the burden of diabetic eye disease.
This proposed system integrates AI driven with healthcare data specific to diabetic patient healthcare to oversee and predict eye health risks. It initiates the processes of collecting data from eye check-up details, blood sugar patterns, physical activity level and dietary information. The AI analyze this data to provide clear insights, analyzing how blood sugar and lifestyle may impact on vision over time. The system classified patients as either at-risk or stable, advising those at risk to see an eye specialist. Continuous health monitoring allows for regular updates and adjustments, creating a feedback loop that supports eye health management. This approach provides diabetic patients with early warnings and customized recommendations to protect their vision.
The novelty of this AI-based system is its capability to predict and prevent diabetic eye problems through integration of various health metrics. By integrating results of eye tests, blood sugar test, physical activity, and diet information, it forms a detailed overview of each patient’s eye health. This system not only evaluates existing risks; it continuously monitors and updates predictions based on real-time alerts and personalized health advice. If eye risks are identified, it prompts early medical action and if not, it encourages ongoing health maintenance. This proactive and adaptive approach empowers with diabetes to effectively protect their vision effectively.
, Claims:1. An AI-driven system for predicting and managing diabetic eye health risks, comprising: a data collection module for receiving healthcare data from diabetic patients, including eye check-up details, blood sugar patterns, physical activity levels, and dietary information; an AI engine for analyzing the collected data and generating a risk assessment for each patient; and a user interface for displaying the risk assessment and providing personalized recommendations.
2. The system as claimed in claim 1, wherein the AI engine utilizes machine learning algorithms trained on a dataset of diabetic patient health information.
3. The system as claimed in claim 1, wherein the risk assessment classifies patients as either at-risk or stable.
4. The system as claimed in claim 3, wherein the system generates alerts and recommends consultation with an eye specialist for at-risk patients.
5. The system as claimed in claim 1, wherein the personalized recommendations include lifestyle modifications, such as dietary changes and increased physical activity.
6. The system as claimed in claim 1, wherein the data collection module receives data from multiple sources, including wearable devices and nutritional tracking apps.
7. The system as claimed in claim 1, wherein the system performs continuous health monitoring and updates risk assessments based on new data inputs.
8. The system as claimed in claim 1, wherein the user interface is accessible through a mobile app or web portal.
9. A method for predicting and managing diabetic eye health risks using an AI-driven system, comprising: collecting healthcare data from diabetic patients; analyzing the collected data using an AI engine to generate a risk assessment; and providing personalized recommendations based on the risk assessment.
10. The method as claimed in claim 9, wherein the step of collecting healthcare data includes receiving data from eye check-ups, blood sugar monitoring devices, physical activity trackers, and dietary logs.