Description:

Artificial intelligence-based model for early diagnosis of Parkinson’s disease using brain imaging
Field of the Invention
[0001] The present disclosure relates to medical imaging and computational diagnostics, more particularly, to artificial intelligence-based early diagnosis of Parkinson’s disease using brain imaging modalities and predictive modelling.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Parkinson’s disease is a progressive neurodegenerative disorder characterized by motor impairments, tremors, rigidity, and non-motor symptoms including cognitive decline and sleep disturbances. Clinical diagnosis is often based on motor manifestations, which typically appear after substantial neuronal degeneration in the substantia nigra and dopaminergic pathways. Delayed diagnosis limits opportunities for early therapeutic interventions and hampers effective disease management. Current diagnostic methods rely heavily on clinical assessments, neurological examinations, and patient history, all of which suffer from subjectivity and late-stage detection.
[0004] Neuroimaging has emerged as a valuable tool for detecting structural and functional changes associated with Parkinson’s disease. Techniques including magnetic resonance imaging, positron emission tomography, and diffusion tensor imaging have been employed to study alterations in brain morphology, connectivity, and dopaminergic activity. However, traditional radiological assessment relies on visual inspection by specialists, which is time-intensive, operator-dependent, and limited in detecting subtle features present in early disease stages. Standard statistical methods employed in neuroimaging research lack scalability and are insufficient to capture the complex, high-dimensional patterns associated with early Parkinsonian changes.
[0005] Recent advances in artificial intelligence and deep learning have enabled automated analysis of complex imaging data. Convolutional neural networks and recurrent architectures have demonstrated potential in classifying neurological disorders from imaging features. Nevertheless, existing systems face limitations including small dataset availability, poor generalizability across imaging modalities, lack of interpretability, and absence of longitudinal predictive modeling. Furthermore, many prototypes remain confined to research environments without integration into clinical workflows.
[0006] Accordingly, there exists a pressing need for a comprehensive artificial intelligence-based system capable of processing diverse brain imaging modalities, extracting multi-dimensional biomarkers, and generating early diagnostic predictions with interpretability. Such a system must incorporate robust preprocessing, advanced feature extraction, longitudinal progression tracking, and transparent classification outputs. The disclosed system addresses these unmet needs by integrating multimodal imaging analysis with deep learning-based diagnostic modeling to provide an accurate, reliable, and clinically applicable solution for early diagnosis of Parkinson’s disease.
Summary
[0007] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[0008] The following paragraphs provide additional support for the claims of the subject application.
[0009] The disclosure pertains to an artificial intelligence-based model for early diagnosis of Parkinson’s disease using brain imaging is disclosed. The system comprises an imaging acquisition module configured to receive data from modalities including MRI, fMRI, PET, and DTI. A preprocessing unit standardizes imaging data through skull stripping, noise removal, and region-of-interest segmentation. A feature extraction engine employing convolutional neural networks and graph-based analysis captures volumetric, textural, and connectivity features of the brain. The extracted features are processed by a diagnostic classification module comprising supervised and deep learning algorithms to generate probability indices of Parkinson’s disease.
[00010] The system further incorporates a longitudinal analysis module configured to assess temporal changes across sequential imaging datasets, enabling prediction of disease progression. A user interface presents diagnostic results, heat maps, and three-dimensional reconstructions of affected brain regions to clinicians. A cloud-based integration module aggregates imaging datasets from multiple centers, supporting continuous retraining and improving generalizability. The system also incorporates explainable AI methods to enhance clinical trust and interpretability.
[00011] The method of operation includes acquiring imaging data, performing preprocessing, extracting features, applying classification models, analyzing longitudinal patterns, and presenting diagnostic outcomes. The disclosed platform integrates data acquisition, preprocessing, deep feature extraction, classification, and visualization into a unified framework. By enabling early detection, progression prediction, and explainability, the system establishes a clinically deployable solution for Parkinson’s disease diagnosis.
Brief Description of the Drawings
[00012] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00013] FIG. 1 illustrates a block diagram of the artificial intelligence-based diagnostic system showing the interrelation of imaging acquisition, preprocessing, feature extraction, classification, longitudinal analysis, and user interface modules, in accordance with the embodiments of the present disclosure.
[00014] FIG. 2 illustrates a method flow diagram showing the sequential operations of the disclosed model, beginning with imaging acquisition, followed by preprocessing, feature extraction, diagnostic classification, progression modelling, and clinical reporting, in accordance with the embodiments of the present disclosure.
[00015] FIG. 3 illustrates a deployment architecture diagram depicting integration of local hospital imaging systems, cloud-based AI servers, and telemedicine endpoints, showing secure data transfer and clinical feedback loops, in accordance with the embodiments of the present disclosure.
Detailed Description
[00016] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00017] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00018] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00019] The disclosed artificial intelligence-based model for early diagnosis of Parkinson’s disease using brain imaging shall now be described in operational detail. The system is structured as an integrated computational and imaging platform wherein imaging data acquisition, preprocessing, feature extraction, classification, and longitudinal analysis are interconnected through controlled data pipelines. The imaging acquisition module is configured to interface with MRI, fMRI, PET, and DTI scanners. Each modality generates volumetric datasets with varying resolutions and contrasts. The module ensures secure transfer of imaging data into the system while maintaining compliance with medical data protocols.
[00020] The preprocessing unit receives imaging data and performs multiple corrective procedures. Skull stripping algorithms remove non-brain tissue artifacts. Motion correction algorithms reduce distortions arising from patient movement. Intensity normalization ensures cross-patient comparability. Segmentation algorithms delineate basal ganglia, substantia nigra, cortical regions, and white matter tracts. These preprocessing steps create standardized inputs compatible with downstream neural network models.
[00021] The feature extraction engine incorporates convolutional neural networks trained on annotated datasets. Volumetric convolutional layers capture structural abnormalities in the substantia nigra. Textural filters analyze heterogeneity of cortical regions. Graph-based modules process connectivity matrices derived from diffusion tensor imaging to quantify white matter integrity. By integrating structural, textural, and connectivity-based features, the engine captures a comprehensive biomarker profile of Parkinson’s pathology.
[00022] The diagnostic classification module receives extracted features and applies supervised machine learning algorithms. Architectures include deep convolutional networks for volumetric patterns, recurrent networks for temporal sequences, and ensemble classifiers for multimodal integration. The module generates probability indices quantifying likelihood of Parkinson’s disease. Confidence intervals are calculated to support clinical decision-making. The system incorporates explainable AI tools

including saliency maps and layer-wise relevance propagation to illustrate regions contributing to predictions.
[00023] The longitudinal analysis module extends diagnostic capabilities by analyzing sequential imaging datasets collected over time. Temporal modeling algorithms quantify rate of volumetric shrinkage, connectivity disruption, and functional signal decline. Predictive models estimate disease progression trajectories, enabling clinicians to forecast severity milestones. Integration with patient health records supports comprehensive disease monitoring.
[00024] The user interface presents outcomes through diagnostic dashboards. Heat maps highlight affected brain regions. Three-dimensional reconstructions provide volumetric visualization. Progression forecasts are displayed alongside probability scores. Wireless integration enables direct transmission of diagnostic outcomes to hospital information systems and telemedicine platforms.
[00025] In a first embodiment, the system operates as a hospital-based diagnostic workstation. Imaging data is acquired from MRI and PET scanners, processed locally, and analyzed by convolutional neural networks. Results are presented through the interface for immediate clinician review. This embodiment benefits from direct integration into hospital workflows.
[00026] In a second embodiment, the system operates as a cloud-based diagnostic service. Imaging data is de-identified and uploaded to cloud servers. Centralized preprocessing, feature extraction, and classification occur on cloud infrastructure. Diagnostic results are transmitted back to clinicians with heat maps and reports. This embodiment benefits from scalability, centralized retraining, and global accessibility.
[00027] In a third embodiment, the system operates as a hybrid telemedicine platform. Imaging data is preprocessed locally to minimize bandwidth, while feature extraction and classification occur remotely. Results are returned in real time to local devices, enabling clinicians in remote settings to access advanced diagnostic support. This embodiment benefits from accessibility in resource-limited regions.
[00028] Alternative operational flows reinforce adaptability. In research contexts, the system aggregates multi-center imaging datasets to refine neural networks. In clinical monitoring, the longitudinal analysis module tracks individual progression, supporting therapeutic adjustments. In pharmaceutical trials, the system standardizes biomarker extraction, enabling consistent evaluation of treatment efficacy.
[00029] Data processing flows are reiterated across contexts. Raw imaging data is preprocessed into standardized volumes. Neural networks extract features and generate classification outputs. Temporal models track progression. Visualization modules translate computational results into interpretable clinical tools. Each repetition strengthens reproducibility and reliability.
[00030] The disclosed artificial intelligence-based diagnostic model thus integrates imaging acquisition, computational analysis, classification, and visualization into a unified framework. Technical benefits include early detection of Parkinson’s disease, improved interpretability, scalability across institutions, and predictive progression modeling. The system provides a clinically deployable platform that advances diagnostic precision and supports early therapeutic interventions, thereby addressing unmet clinical needs in neurodegenerative disease management.
[00031] Figure 1 provides a block diagram illustrating the overall structure of the artificial intelligence-based diagnostic system for Parkinson’s disease detection. The imaging acquisition module interfaces with MRI, fMRI, PET, and DTI scanners. Data is transferred to the preprocessing unit, where skull stripping, normalization, and segmentation are performed. Pre-processed images are transmitted to the feature extraction engine, where convolutional neural networks and connectivity analysers derive biomarkers. These features are supplied to the diagnostic classification module, which applies supervised learning algorithms to generate probability indices. Outputs are then passed to the longitudinal analysis module, where temporal patterns are studied for disease progression forecasts. Results are displayed through the user interface, which also supports secure transmission to external hospital systems. The block diagram demonstrates modular arrangement with linear and feedback interconnections, establishing a comprehensive diagnostic pipeline. Each module provides distinct technical benefits: preprocessing ensures standardized input, feature extraction enhances sensitivity, classification provides immediate diagnostic probability, and longitudinal analysis supplies predictive insight. Integration across all modules creates a robust, clinically deployable solution.
[00032] Figure 2 provides a method flow diagram showing the operational pathway of the diagnostic process. Imaging data is first acquired from brain scanners and input into the preprocessing stage. Here, noise correction, intensity normalization, and segmentation produce standardized brain volumes. The standardized data is directed into the feature extraction stage, where neural networks extract volumetric, textural, and connectivity-based biomarkers. These features are analyzed by the diagnostic classification module, which produces probability scores of Parkinson’s disease presence. Subsequently, the longitudinal analysis module evaluates sequential scans to track progression. Finally, results are delivered through the reporting stage, where diagnostic probability, heat maps, and progression forecasts are presented to clinicians. The method flow diagram emphasizes sequential and logical dependencies across steps. By illustrating linear progression, the figure highlights how raw imaging is transformed into actionable diagnostic outcomes. This representation underscores efficiency, reproducibility, and adaptability of the disclosed system.
[00033] Figure 3 provides a deployment architecture diagram illustrating system-level integration across clinical environments. Local hospital scanners generate imaging data, which is securely transmitted to the preprocessing and feature extraction layers. In one configuration, local servers process data and transmit features to cloud-based AI servers. In another configuration, de-identified raw imaging data is uploaded to centralized servers for full analysis. The AI servers perform classification, progression modeling, and explainable analysis. Results are returned to hospital workstations, telemedicine dashboards, and patient electronic health records. The deployment diagram also shows feedback loops for continuous retraining of models using multi-center datasets. This arrangement demonstrates scalability and adaptability of the diagnostic system. The deployment architecture provides resilience by enabling both local and remote processing. Clinical benefit arises from integration into hospital workflows, while research benefit derives from global dataset aggregation.
[00034] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00035] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
 

Claims
I/We Claim:
1. An artificial intelligence-based diagnostic system for early detection of Parkinson’s disease using brain imaging, comprising: an imaging acquisition module configured to receive brain scans from modalities selected from magnetic resonance imaging, functional magnetic resonance imaging, positron emission tomography, or diffusion tensor imaging; a preprocessing unit configured to normalize intensity values, remove noise, and segment anatomical regions of interest associated with basal ganglia, substantia nigra, and cortical areas; a feature extraction engine incorporating convolutional neural networks trained to capture volumetric, textural, and connectivity-related features of brain tissue; a diagnostic classification module employing supervised machine learning algorithms configured to generate probability scores of Parkinson’s disease presence; a longitudinal analysis module configured to compare temporal imaging data for progressive changes; and a user interface configured to display diagnostic outcomes, probability indices, and progression forecasts, wherein integration of imaging and computational analysis provides early diagnostic support.
2. The system of claim 1, wherein the preprocessing unit comprises skull stripping, motion correction, and intensity standardization algorithms, thereby ensuring consistent imaging input for reliable neural network analysis.
3. The system of claim 1, wherein the feature extraction engine further incorporates graph-based connectivity analysis of white matter tracts derived from diffusion tensor imaging, thereby enabling assessment of microstructural changes associated with Parkinson’s disease.
4. The system of claim 1, wherein the diagnostic classification module comprises deep learning architectures including convolutional neural networks, recurrent neural networks, and ensemble classifiers, thereby enhancing diagnostic accuracy across heterogeneous imaging datasets.
5. The system of claim 1, wherein the longitudinal analysis module incorporates time-series modeling algorithms configured to evaluate rate of change in imaging biomarkers, thereby supporting predictive assessment of disease progression.
6. The system of claim 1, wherein the user interface further incorporates visualization tools configured to present heat maps, saliency maps, and three-dimensional reconstructions of affected brain regions, thereby providing interpretability of diagnostic predictions.
7. The system of claim 1, wherein the system further comprises a cloud-based integration module configured to aggregate de-identified brain imaging datasets from multiple clinical centers, thereby supporting continuous retraining of diagnostic models for improved robustness.
8. The system of claim 1, wherein the artificial intelligence framework incorporates explainable AI methods including layer-wise relevance propagation and attention mechanisms, thereby providing transparency into the decision-making process and supporting clinical adoption.
9. The system of claim 1, wherein the diagnostic outcomes are transmitted through secure communication protocols to electronic health record systems and telemedicine platforms, thereby enabling integration into standard clinical workflows.
10. The system of claim 1, wherein integration of imaging preprocessing, deep feature extraction, machine learning classification, longitudinal tracking, and interpretable visualization within a unified platform provides a comprehensive diagnostic solution for early detection and monitoring of Parkinson’s disease.

Artificial intelligence-based model for early diagnosis of Parkinson’s disease using brain imaging
Abstract
An artificial intelligence-based model for early diagnosis of Parkinson’s disease using brain imaging is disclosed. The system comprises an imaging acquisition module, a preprocessing unit for normalization and segmentation, and a feature extraction engine employing convolutional neural networks and connectivity analysis. A diagnostic classification module applies machine learning algorithms to generate probability indices of Parkinson’s disease. A longitudinal analysis module evaluates progression over time. Results are displayed with heat maps and three-dimensional reconstructions and transmitted to external systems via secure protocols. Cloud-based integration enables continuous retraining across datasets. Explainable AI methods enhance transparency. Integration of multimodal imaging, advanced machine learning, and interpretable outputs within a unified platform provides accurate, early, and clinically applicable diagnosis of Parkinson’s disease.
Fig. 1

  , Claims:Claims
I/We Claim:
1. An artificial intelligence-based diagnostic system for early detection of Parkinson’s disease using brain imaging, comprising: an imaging acquisition module configured to receive brain scans from modalities selected from magnetic resonance imaging, functional magnetic resonance imaging, positron emission tomography, or diffusion tensor imaging; a preprocessing unit configured to normalize intensity values, remove noise, and segment anatomical regions of interest associated with basal ganglia, substantia nigra, and cortical areas; a feature extraction engine incorporating convolutional neural networks trained to capture volumetric, textural, and connectivity-related features of brain tissue; a diagnostic classification module employing supervised machine learning algorithms configured to generate probability scores of Parkinson’s disease presence; a longitudinal analysis module configured to compare temporal imaging data for progressive changes; and a user interface configured to display diagnostic outcomes, probability indices, and progression forecasts, wherein integration of imaging and computational analysis provides early diagnostic support.
2. The system of claim 1, wherein the preprocessing unit comprises skull stripping, motion correction, and intensity standardization algorithms, thereby ensuring consistent imaging input for reliable neural network analysis.
3. The system of claim 1, wherein the feature extraction engine further incorporates graph-based connectivity analysis of white matter tracts derived from diffusion tensor imaging, thereby enabling assessment of microstructural changes associated with Parkinson’s disease.
4. The system of claim 1, wherein the diagnostic classification module comprises deep learning architectures including convolutional neural networks, recurrent neural networks, and ensemble classifiers, thereby enhancing diagnostic accuracy across heterogeneous imaging datasets.
5. The system of claim 1, wherein the longitudinal analysis module incorporates time-series modeling algorithms configured to evaluate rate of change in imaging biomarkers, thereby supporting predictive assessment of disease progression.
6. The system of claim 1, wherein the user interface further incorporates visualization tools configured to present heat maps, saliency maps, and three-dimensional reconstructions of affected brain regions, thereby providing interpretability of diagnostic predictions.
7. The system of claim 1, wherein the system further comprises a cloud-based integration module configured to aggregate de-identified brain imaging datasets from multiple clinical centers, thereby supporting continuous retraining of diagnostic models for improved robustness.
8. The system of claim 1, wherein the artificial intelligence framework incorporates explainable AI methods including layer-wise relevance propagation and attention mechanisms, thereby providing transparency into the decision-making process and supporting clinical adoption.
9. The system of claim 1, wherein the diagnostic outcomes are transmitted through secure communication protocols to electronic health record systems and telemedicine platforms, thereby enabling integration into standard clinical workflows.
10. The system of claim 1, wherein integration of imaging preprocessing, deep feature extraction, machine learning classification, longitudinal tracking, and interpretable visualization within a unified platform provides a comprehensive diagnostic solution for early detection and monitoring of Parkinson’s disease.