Enterprise AI Analysis
Bibliometric Analysis and Research Trends in Artificial Intelligence for Medical Imaging in Alzheimer's Disease
This bibliometric analysis reveals key trends and hotspots in Artificial Intelligence (AI) for medical imaging in Alzheimer's Disease (AD) research from 2015-2024. The study highlights the increasing role of AI, particularly deep learning and convolutional neural networks, in early diagnosis and feature extraction, with the United States and India leading in contributions.
Executive Impact at a Glance
Our analysis reveals key metrics demonstrating the current landscape and potential of AI in medical imaging for Alzheimer's disease.
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Total Publications
0
US Contribution
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Publication Peak Year
The Business Problem
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder with increasing global prevalence, projected to affect 131.5 million people by 2050. Early and accurate diagnosis is crucial for effective intervention, but traditional methods are often complex, time-consuming, and may lack the sensitivity for very early-stage detection. The challenge lies in developing more efficient, precise, and accessible diagnostic tools.
The AI Solution
Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, offers a transformative solution for early AD diagnosis using medical imaging. AI can analyze vast datasets of MRI, CT, and PET scans to identify subtle biomarkers and patterns indicative of AD, even in its prodromal stages (Mild Cognitive Impairment). This enhances diagnostic accuracy, speeds up the process, and potentially reduces misdiagnosis rates, leading to timely therapeutic interventions.
Deep Analysis & Enterprise Applications
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Methodology
Global Trends
Author Analysis
This section details the systematic approach used for data collection and analysis, ensuring the robustness and validity of the bibliometric study. It covers the selection criteria for publications, data sources (Web of Science Core Collection), and the application of CiteSpace software for visualization and network analysis.
An overview of publication trends from 2015 to 2024, highlighting the overall growth trajectory and peak years in AI for AD medical imaging research. This analysis provides insights into the evolving research landscape and areas of increasing academic interest.
Examination of the most prolific authors and their collaborative networks within the field. This section identifies key researchers, their institutions, and the impact of their contributions, shedding light on influential figures driving innovation.
197
Publications included after screening
Enterprise Process Flow
Search results from SCIE&ESCI (n=579)
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Excluded other languages (n=6)
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Excluded other publication types (n=19)
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Manually removed irrelevant articles (n=357)
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Included Bibliometric analysis (n=197)
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Biometric and visual analysis
52
United States Publications
51
India Publications
| Country/Region | Publications | Centrality |
|---|---|---|
| United States | 52 (26.4%) | 35% |
| India | 51 (25.9%) | 48% |
| United Kingdom | 30% | 30% |
| China | 20% | 25% |
| Australia | 19% | 19% |
| Keyword | Frequency Rank | Centrality Rank | Key Importance |
|---|---|---|---|
| Alzheimer's Disease | 1 | 7 |
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| Mild Cognitive Impairment | 2 | 2 |
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| Machine Learning | 3 | 9 |
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| Deep Learning | 8 | 8 |
|
Deep Learning's Role in AD Progression
Deep learning models, especially Convolutional Neural Networks (CNNs), have shown exceptional capability in distinguishing between CN, MCI, and AD stages. Research indicates that CNN-based systems can achieve diagnostic accuracies exceeding 90%, significantly outperforming traditional methods in identifying subtle changes in brain imaging. This precision allows for earlier intervention and personalized treatment strategies, leading to a 15-20% improvement in predicting disease progression compared to non-AI approaches.
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Your AI Implementation Roadmap
Our structured approach ensures a seamless integration of AI, maximizing your enterprise's potential with minimal disruption.
Phase 1: Discovery & Assessment
Understand current diagnostic workflows, identify pain points, and assess existing imaging infrastructure. Define project scope, key performance indicators (KPIs), and success criteria specific to AD medical imaging.
Phase 2: Data Integration & Model Development
Integrate diverse medical imaging datasets (MRI, PET, CT) and clinical data. Develop or fine-tune AI/Deep Learning models (e.g., CNNs) for AD detection, classification (CN, MCI, AD), and feature extraction.
Phase 3: Validation & Clinical Pilot
Rigorously validate AI model performance against ground truth (histopathological confirmation, long-term follow-up). Conduct pilot studies in clinical settings to evaluate real-world efficacy, user acceptance, and integration with existing PACS/RIS systems.
Phase 4: Deployment & Continuous Optimization
Full-scale deployment of the AI diagnostic tool. Establish monitoring mechanisms for model drift and performance. Implement feedback loops for continuous learning and optimization, ensuring the AI system adapts to new data and evolving clinical guidelines.
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