ARTIFICIAL INTELLIGENCE-POWERED DIAGNOSTIC TOOL FOR DETECTION OF ALZHEIMER'S DISEASE

Abstract

ABSTRACT The present invention discloses an artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease. The invention integrates brain imaging data (e.g., MRI, PET) with cognitive assessments to provide a comprehensive assessment of an individual's risk. Advanced AI algorithms analyze the combined dataset, identifying subtle patterns and correlations between brain changes and cognitive decline, potentially revealing early disease markers. This multimodal approach enhances diagnostic accuracy and sensitivity, enabling timely intervention and improved patient outcomes. The invention's adaptability to various settings facilitates widespread use in clinical diagnostics, drug development, and personalized medicine, contributing to the fight against Alzheimer's disease.

Specification

Description:ARTIFICIAL INTELLIGENCE-POWERED DIAGNOSTIC TOOL FOR DETECTION OF ALZHEIMER'S DISEASE

Field of Invention
The present invention relates to the diagnosis of Alzheimer's disease. More particularly, an artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease using brain imaging and cognitive assessments.

Background of the Invention
Alzheimer's disease is a brain condition that causes a gradual decline in memory, thinking, learning, and organizing skills. Symptoms worsen over time and can eventually affect a person's ability to perform basic daily activities. Alzheimer's disease can be detected using a variety of tests and evaluations, including: Blood tests, Brain scans, Biomarker tests, Psychiatric evaluation, Cognitive testing, other ways to detect Alzheimer's disease include: Asking questions about overall health, diet, and changes in behaviour and personality and ordering standard medical tests, such as blood and urine tests.
Numerous research efforts have explored the use of AI in Alzheimer's disease detection, often focusing on either brain imaging or cognitive assessments individually. Some notable examples from research journals and conference proceedings include:
• Brain Imaging-based Approaches:
• Spasov et al. (2019) "Alzheimer's disease neuroimaging initiative" - This study employed deep learning models on MRI scans to predict Alzheimer's disease progression.
• Liu et al. (2018), "Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease," investigated combining various brain imaging modalities (MRI, PET) using machine learning for improved diagnosis.
• Cognitive Assessment-based Approaches:
• Hodges et al. (2018) "The Addenbrooke's Cognitive Examination Revised (ACER): a brief cognitive test battery for dementia screening" - This research developed and validated a cognitive test battery for dementia screening, which could be used as input for AI models.
• Castillo-Sánchez et al. (2020) "Machine learning models for predicting conversion from mild cognitive impairment to Alzheimer's disease" - This study used machine learning on cognitive test data to predict the conversion from mild cognitive impairment to Alzheimer's.
While these prior art approaches demonstrated promising results, they typically suffer from a key limitation:
• Reliance on a Single Data Source: Most existing methods focus on either brain imaging or cognitive assessments in isolation. This can lead to incomplete or less accurate diagnoses, as each data source provides unique but potentially complementary information about the disease process. Brain imaging might reveal structural or functional changes, while cognitive assessments capture behavioural and cognitive deficits associated with Alzheimer's.
Technical Drawback: • Limited Diagnostic Accuracy: By relying on a single data source, prior-art methods might miss subtle early signs of Alzheimer's that could be detected by combining information from brain imaging and cognitive assessments. This can result in delayed diagnosis and missed opportunities for early intervention.
• US Patent No. 10,733,040 B2: "Systems and methods for early detection of Alzheimer's disease" (Granted) - This patent describes a method for detecting Alzheimer's disease using brain imaging data and machine learning algorithms.
• US Patent No. 9,824,134 B2: "Method for detecting Alzheimer's disease based on brain imaging data and clinical data" (Granted) - This patent discloses a method for diagnosing Alzheimer's disease by combining brain imaging data with clinical information using a machine learning model.
• US Patent Application No. 2020/0342470 A1: "Systems and methods for assessing cognitive impairment" (Published) - This patent application describes a system for evaluating cognitive impairment using a combination of cognitive tests and physiological measurements.
A standard limitation observed in many existing patents is the reliance on a single data source for Alzheimer's disease detection. Some patents focus primarily on brain imaging data, while others may prioritize clinical or cognitive assessment data.
Incomplete Picture of Disease Progression: By relying on a single data source, these prior art methods may not capture the full spectrum of changes associated with Alzheimer's disease. Brain imaging might reveal structural or functional abnormalities, but cognitive assessments can provide insights into the behavioral and cognitive deficits that impact a patient's daily life. Analyzing these data sources in isolation may lead to less accurate or delayed diagnoses, hindering early intervention efforts.
The present invention aims to overcome this drawback by integrating brain imaging and cognitive assessment data, offering a more comprehensive approach to early Alzheimer's disease detection.

Objectives of the Invention
The prime objective of the present invention is to provide an artificial intelligence-powered diagnostic tool for detection of Alzheimer's disease.

Another object of this invention is to provide the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease using brain imaging and cognitive assessments.

Another objective of the present invention is to provide the artificial intelligence-powered diagnostic tool for detection of Alzheimer's disease where by integrating both data sources, the invention aims to provide a more accurate and comprehensive assessment of an individual's risk of developing Alzheimer's.

Another objective of the present invention is to provide the artificial intelligence-powered diagnostic tool for detection of Alzheimer's disease where the tool utilizes advanced AI algorithms to analyze and interpret complex patterns within brain imaging data (such as MRI or PET scans) and cognitive test results.

Yet another object of this invention is to provide the artificial intelligence-powered diagnostic tool for detection of Alzheimer's disease by enabling early and accurate diagnosis, facilitate timely intervention with potential disease-modifying therapies, improve patient outcomes through better symptom management and delayed institutionalization, and contribute to the advancement of research by identifying early disease markers and tracking disease progression.

These and other objects of the present invention will be apparent from the drawings and descriptions herein. Every object of the invention is attained by at least one embodiment of the present invention.
Summary of the Invention
In one aspect of the present invention provides the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease using brain imaging and cognitive assessments, it is trained to recognize correlations and interactions between brain changes and cognitive decline, potentially revealing novel biomarkers for early detection.
In one of the aspects, in the present system, the ability to combine and analyze multiple data sources has the potential to significantly enhance the sensitivity and specificity of Alzheimer's diagnosis, particularly in its early stages, this early detection could enable timely intervention with potential disease-modifying therapies or lifestyle changes, leading to improved patient outcomes and quality of life.
In one of the aspects, in the present invention, the provides the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease comprises of brain imaging acquisition, cognitive assessment, data integration, AI model analysis, and reporting and feedback.

Brief Description of Drawings
The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present disclosure. Further objectives and advantages of this invention will be more apparent from the ensuing description when read in conjunction with the accompanying drawing and wherein:
Figure 1 illustrates the Alzheimer's Diagnostic Process according to the preferred embodiment of the present invention.
Figure 2 illustrates the Flowchart for Alzheimer's Diagnostic algorithm according to an embodiment of the present invention.

DETAIL DESCRIPTION OF INVENTION
Unless the context requires otherwise, throughout the specification which follow, the word "comprise" and variations thereof, such as, "comprises" and "comprising" are to be construed in an open, inclusive sense that is as "including, but not limited to".
In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.

Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.

The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered 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 spirit and scope of the present disclosure as defined by the appended claims.

The headings and abstract of the invention provided herein are for convenience only and do not interpret the scope or meaning of the embodiments. Reference will now be made in detail to the exemplary embodiments of the present invention.
The present invention discloses the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease. This comprises a sophisticated system that leverages brain imaging data (such as MRI or PET scans) and cognitive assessments to identify subtle indicators of Alzheimer's disease in its early stages. It utilizes advanced AI algorithms to analyze and interpret complex patterns within brain scans and cognitive test results. By integrating these two data sources, the tool aims to provide a more accurate and comprehensive assessment of an individual's risk of developing Alzheimer's, enabling earlier intervention and potentially improving patient outcomes.

In describing the preferred embodiment of the present invention, reference will be made herein to like numerals refer to like features of the invention.

According to preferred embodiment of the invention, referring to Figure 1, artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease comprises of Brain Imaging System (Part A), Cognitive Assessment Module (Part B), Data Integration and Processing Unit (Part C), AI-Powered Diagnostic Engine (Part D), and User Interface and Reporting System (Part E).


According to another embodiment of the invention, referring to Figure 2, the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease works in the following steps:
Step 1: Brain Imaging Acquisition
Sub-process A 1.1: Capture high-resolution structural brain images using MRI. Sub-process A1.2: Capture functional brain images using PET to detect amyloid plaques or other markers of Alzheimer's.
Step 2: Cognitive Assessment
Sub-process B1.1: Administer cognitive tests assessing memory, attention, language, and executive function.
Sub-process B1.2: Collect and store cognitive performance data for analysis.
Step 3: Data Integration
Sub-process C1.1: Fuse brain imaging data with cognitive assessment results to form a unified dataset.
Sub-process C2.1: Pre-process the data to standardize formats, reduce noise, and extract relevant features.
Step 4: AI Model Analysis
Sub-process D1.1: Apply the machine learning model to analyze the combined dataset, recognizing patterns indicative of early-stage Alzheimer's.
Sub-process D2.1: Extract key diagnostic features from brain imaging and cognitive data.
Sub-process D3.1: Generate an Alzheimer's risk score or prediction using the AI diagnostic algorithm.
Step 5: Reporting and Feedback
Sub-process E1.1: Display the diagnostic results through the visualization module for clinician interpretation.
Sub-process E2.1: Generate and provide a comprehensive diagnostic report, including AI-based risk assessments and recommendations for further action.

According to another embodiment of the invention, the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease has synergistic interaction in the following manner:
• Multimodal Data Fusion: Integrating brain imaging and cognitive assessment data results in a more comprehensive analysis of Alzheimer's progression, capturing information that a single modality might miss.
• AI Analysis: The machine learning model processes the unified dataset to detect complex patterns and subtle biomarkers that could indicate early Alzheimer's, which is not apparent through traditional diagnostic methods.
• Real-time Diagnosis and Reporting: The entire system, from data acquisition to AI analysis and report generation, works together to provide early, accurate, and actionable insights, which helps in timely intervention.
By synergistically combining these parts and processes, the invention achieves enhanced diagnostic accuracy and provides a solution to the early detection of Alzheimer's disease.

According to another embodiment of the invention, the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease combines structural MRI scans with a standardized cognitive test battery, allowing the AI model to correlate brain changes with cognitive deficits.

According to another embodiment of the invention, it integrates functional PET scans with computerized cognitive tests, linking abnormal metabolic patterns or amyloid accumulation to cognitive impairments.

According to another embodiment of the invention, it combines imaging modalities like MRI, PET, and EEG with data from wearable sensors, providing a comprehensive overview of brain health and cognitive function. While the optimal approach may vary based on cost and accessibility, the best method could involve combining structural MRI with a standardized cognitive battery and utilizing a deep learning-based AI model trained on a diverse dataset. This approach leverages the strengths of both data sources while remaining cost-effective and widely accessible. The deep learning capabilities enable the identification of complex patterns within the data, potentially leading to improved early detection of disease.

According to another embodiment of the invention, the artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease has one prominent industrial application in clinical diagnostics and healthcare:
• Early Diagnosis and Screening: The invention could be utilized in hospitals, clinics, and research centers as a powerful tool for early detection and screening of Alzheimer's disease. Its ability to identify subtle signs of the disease before significant cognitive decline could enable timely intervention and potentially improve patient outcomes.
• Clinical Trials and Drug Development: Pharmaceutical companies and research institutions could employ this tool in clinical trials to assess the efficacy of new Alzheimer's treatments. The invention's sensitivity to early disease changes could provide valuable insights into treatment response and aid in developing more effective therapies.
• Personalized Medicine: The invention could generate comprehensive risk profiles based on brain imaging and cognitive data, paving the way for personalized treatment plans and preventive strategies. This could lead to more targeted interventions and improved patient care.
• Remote Monitoring and Telemedicine: The invention's potential for adaptation to various settings, including remote monitoring and telemedicine platforms, could expand access to early Alzheimer's detection and management, particularly in underserved areas or for individuals with limited mobility.

Although a preferred embodiment of the invention has been illustrated and described, it will at once be apparent to those skilled in the art that the invention includes advantages and features over and beyond the specific illustrated construction. Accordingly it is intended that the scope of the invention be limited solely by the scope of the hereinafter appended claims, and not by the foregoing specification, when interpreted in light of the relevant prior art.
, Claims:We Claim;
1. An artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease comprising of
Part A: a Brain Imaging System;
Part B: a Cognitive Assessment Module;
Part C: a Data Integration and Processing Unit;
Part D: an AI-Powered Diagnostic Engine; and
Part E: and a User Interface and Reporting System.
2. The artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease as claimed in claim 1, wherein the tool works in the following steps:
Step 1: Brain Imaging Acquisition
Sub-process A 1.1: Capture high-resolution structural brain images using MRI,
Sub-process A1.2: Capture functional brain images using PET to detect amyloid plaques or other markers of Alzheimer's;
Step 2: Cognitive Assessment
Sub-process B1.1: Administer cognitive tests assessing memory, attention, language, and executive function,
Sub-process B1.2: Collect and store cognitive performance data for analysis;
Step 3: Data Integration
Sub-process C1.1: Fuse brain imaging data with cognitive assessment results to form a unified dataset,
Sub-process C2.1: Pre-process the data to standardize formats, reduce noise, and extract relevant features;
Step 4: AI Model Analysis
Sub-process D1.1: Apply the machine learning model to analyse the combined dataset, recognizing patterns indicative of early-stage Alzheimer's,
Sub-process D2.1: Extract key diagnostic features from brain imaging and cognitive data,
Sub-process D3.1: Generate an Alzheimer's risk score or prediction using the AI diagnostic algorithm;
Step 5: Reporting and Feedback
Sub-process E1.1: Display the diagnostic results through the visualization module for clinician interpretation,
Sub-process E2.1: Generate and provide a comprehensive diagnostic report, including AI-based risk assessments and recommendations for further action.
3. The artificial intelligence-powered diagnostic tool for early detection of Alzheimer's disease as claimed in claim 1, wherein the tool combines structural MRI scans with a standardized cognitive test battery, allowing the AI model to correlate brain changes with cognitive deficits.

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