Wearable sensors + AI detect early signs of neurological disease

This research proposes a comprehensive framework for the early prediction of neurodegenerative diseases (such as Parkinson’s and ALS) by combining wearable technology with Artificial Intelligence. Addressing the critical gap in diagnosing these conditions during their "prodromal" (pre-symptomatic) phase, the system utilizes a mobile application linked to wearable sensors that continuously monitor vital signs and physiological data.

The core innovation is the use of Recurrent Neural Networks (RNNs) and transfer learning techniques to analyze these longitudinal data streams. Unlike traditional "snapshot" clinical exams, this system detects subtle, non-linear deviations in patient data—such as micro-changes in gait or sleep patterns—that serve as early warning signs of neural decline. The framework also integrates secure authentication (Aadhaar-based) to ensure data privacy while enabling remote monitoring. Preliminary testing with simulated patient data verified the workflow's functionality, suggesting that such a system could democratize access to early diagnosis, particularly in underserved communities where access to neurologists is limited.


Read original article at https://www.researchgate.net/publication/395091085_Smart_Healthcare_Harnessing_AI_for_Early_prediction_of_Neurodegenerative_disease

 

Comments

Post a Comment

Popular posts from this blog

Generative AI Will Transform Healthcare, But Only If We Get the Governance Right

Image
  India is entering a more serious phase of healthcare AI. With ABDM’s digital rails in place, and policy and governance frameworks beginning to take shape, the country is now better positioned to use generative AI at scale. This is no longer just a future idea. It is already entering real workflows such as consultation summaries, discharge drafts, coding support, patient communication, and claims documentation. That makes the conversation more urgent. In healthcare, the real question is not whether generative AI is impressive, but whether it can be trusted in systems where errors have clinical, operational, financial, and legal consequences. AI will shape healthcare meaningfully only if governance is built in from the start, not added later as an afterthought. The opportunity is real The case for generative AI in healthcare is strong. It can reduce administrative burden, improve documentation quality, support coding and claims workflows, simplify patient communication, and help cl...
Read more

AI in healthcare Insights: 20th November - 26th November' 2025

Image
  Gel-free 3D-printed electrodes + AI detect arrhythmias in wearables Researchers at Simon Fraser University have developed a heart monitoring system combining 3D printing and AI to improve cardiac diagnostics. The system uses reusable, dry 3D-printed electrodes with an origami-inspired design that adheres to the skin without sticky gel, addressing issues of patient discomfort and signal degradation over time.  The embedded AI software is capable of pre-diagnosing up to 10 types of arrhythmias in real-time. Clinical testing indicates the design improves patient compliance compared to traditional Holter monitors. The system is designed to allow test results to be shared electronically with physicians for confirmation, aiming to expand access to monitoring in remote and underserved communities. Read the original article at https://www.sfu.ca/sfunews/media/media-releases/2025/11/ai-at-the-heart-of-new-sfu-gel-free-ecg-system-for-faster-diagno.html Our Opinion: This shift solve...
Read more

Clinical AI & MedTech Insights: January 22 - January 28

Image
  UK trust tests AI safety and fairness across all patients As artificial intelligence tools become common in hospitals ensuring they work equally well for everyone is critical. The University Hospitals of Leicester has launched a major trial to assess the fairness of AI diagnostic models across diverse patient groups. The study aims to identify and mitigate algorithmic bias where AI performs well for one demographic but fails for another. This often happens when models are trained on narrow datasets that do not represent the full population. By rigorously testing these tools on a wide range of real world patient data the trust hopes to set a new standard for safe and equitable deployment. This initiative addresses the growing concern that medical AI could inadvertently worsen health disparities if left unchecked. The ultimate goal is to validate that these powerful diagnostic aids deliver the same high level of accuracy for every patient regardless of their background or medic...
Read more