Welcome to Neuro-EarlyDetect, a research-driven project aiming to detect neurodegenerative diseases at their earliest stages using non-invasive methods. By leveraging cutting-edge technologies and cross-disciplinary collaboration, we hope to make earlier diagnosis possible—paving the way for better patient outcomes and a brighter, healthier future.
- Early Intervention: Catching symptoms at the earliest stages can significantly slow disease progression.
- Non-Invasive Approach: Traditional tests can be uncomfortable and time-consuming. We aim to reduce patient burden and improve accessibility.
- Collaboration is Key: We bring together neuroscientists, data scientists, medical professionals, and engineers to create a powerful, unified approach.
The Neuro-EarlyDetect project focuses on researching novel biomarkers and innovative detection methods to spot early signs of Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. With a year-long collaboration plan, we’ll combine expertise from multiple fields to create practical tools, build proof-of-concept prototypes, and share findings with the broader research community.
We’re inviting passionate individuals—researchers, clinicians, developers, data scientists, and students—to join us in a one-year plan to push the boundaries of neurodegenerative disease detection.
- Check the Roadmap: See how we’ll structure our project sprints and milestones.
- Reach Out: Send an email or message expressing your interest.
- Join the Private Slack: Once we connect, we’ll add you to our private Slack workspace where we coordinate tasks, share updates, and collaborate in real time.
Here’s a glimpse of our yearly plan to develop, test, and refine our non-invasive detection methods:
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Month 1–2: Ideation and Onboarding
- Brainstorm techniques for early detection and form research sub-teams.
- Familiarize new collaborators with existing literature, tools, and processes.
- Set up Slack channels, weekly stand-ups, and project management boards.
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Month 3–4: Data Gathering & Preliminary Experiments
- Collect or source datasets (EEG, MRI, wearable sensor data, etc.).
- Develop preliminary machine learning models or signal processing pipelines.
- Identify potential biomarkers and refine hypotheses.
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Month 5–6: Prototype Development
- Build and test prototypes for early detection (hardware, software, or both).
- Perform initial validation on small-scale clinical or simulated data.
- Document findings in shared repositories for peer review.
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Month 7–8: Optimization & Validation
- Improve accuracy, speed, and usability of prototypes.
- Test in more extensive or diverse settings (larger datasets).
- Discuss feedback in weekly Slack sessions and pivot as needed.
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Month 9–10: Analysis & Iteration
- Deep-dive into performance metrics.
- Initiate advanced statistical or machine learning methods to enhance detection.
- Publish intermediate results in relevant forums or internal reports.
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Month 11–12: Publication & Future Roadmap
- Consolidate findings into research papers, open-access data resources, and/or patents.
- Present results at conferences or through webinars.
- Outline the next phase of research, additional collaborations, and extended goals.
- Open Collaboration: Inclusive environment supporting all skill levels.
- Transparent Processes: Regular updates, milestone tracking, and open data (where possible) for reproducibility.
- Cutting-Edge Tools: Integration of AI/ML, advanced signal processing, and innovative hardware solutions.
- Community Focus: Aspiring to make a meaningful contribution to public health and patient communities.
Ready to contribute? We welcome all forms of collaboration—whether it’s brainstorming ideas, sharing relevant datasets, coding, or working on analytics pipelines. Please see our CONTRIBUTING.md (coming soon) for guidelines on pull requests, code style, and issue reporting.
- Email: [email protected]
- Private Slack: Email or message us your details, and we’ll send you an invite link.
- Twitter: @NeuroEarlyDetect (for quick project updates)
Have questions, feedback, or want to sign up? Reach out directly—let’s collaborate!
This project is licensed under the MIT License. Feel free to use, modify, or distribute, but please do so responsibly and credit the contributors.
| Title | Authors | Published In | Year | Abstract | Link |
|---|---|---|---|---|---|
| Advancements of Deep Learning in Medical Imaging for Neurodegenerative Diseases | Loveleen Gaur, Patrick Siarry, Ajith Abraham, Oscar Castillo | Frontiers in Neuroscience | 2024 | This editorial discusses the integration of deep learning techniques in medical imaging to enhance the diagnosis and understanding of neurodegenerative diseases such as Alzheimer's and Parkinson's. | Read Full Paper |
| Artificial Intelligence in Neurodegenerative Diseases: Opportunities and Challenges | Not specified | Springer | 2024 | This chapter examines the application of AI algorithms, including supervised and unsupervised learning, in classifying patients with neurodegenerative diseases based on clinical data and brain imaging. | Read Full Paper |
| A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis | Haocong Rao, Minlin Zeng, Xuejiao Zhao, Chunyan Miao | arXiv | 2024 | This survey reviews the progress of AI techniques applied to diagnosing neurodegenerative diseases through gait analysis, discussing challenges and future directions in this emerging field. | Read Full Paper |
| Quantum AI for Alzheimer's Disease Early Screening | Giacomo Cappiello, Filippo Caruso | arXiv | 2024 | This paper investigates the application of quantum machine learning algorithms to analyze handwriting samples for early screening of Alzheimer's disease, comparing their performance with classical methods. | Read Full Paper |
| Alzheimer's Disease Diagnosis Using Machine Learning: A Review | Nair Bini Balakrishnan, P. S. Sreeja, Jisha Jose Panackal | arXiv | 2023 | This review analyzes various machine learning techniques employed in diagnosing Alzheimer's disease, emphasizing the potential of deep learning methods in feature extraction and classification. | Read Full Paper |
| AI for the Prediction of Early Stages of Alzheimer's Disease from Neuroimaging Biomarkers | Thorsten Rudroff, Oona Rainio, Riku Klén | arXiv | 2024 | This narrative review summarizes the current state of AI applications in neuroimaging for early Alzheimer's disease prediction, highlighting the potential of AI techniques in improving early diagnosis, prognosis, and management. | Read Full Paper |
| AI and Non-AI Assessments for Dementia | Mahboobeh Parsapoor, Hamed Ghodrati, Vincenzo Dentamaro, et al. | arXiv | 2023 | This paper reviews various AI-powered and non-AI assessments for dementia, providing valuable information about different assessment tools for both the AI and medical communities. | Read Full Paper |
| From Pixels to Prognosis: AI-Driven Insights into Neurodegenerative Diseases | Not specified | Medical Research Archives | 2024 | This article discusses how advancements in artificial intelligence, particularly machine learning and deep learning techniques, have shown promising results in improving the diagnostic accuracy and evaluation of neurodegenerative diseases. | Read Full Paper |