- Friday, Sep 5th, 2025
| TITLE | AI-Driven Early Diagnosis of Neurological Disorders from Imaging and Clinical Data |
|---|---|
| ABSTRACT | Neurological disorders—like Alzheimer’s, Parkinson’s, and Multiple Sclerosis—aren’t just medical diagnoses. They’re life-altering experiences that slowly and silently reshape the lives of millions of people and their loved ones around the world. These conditions steal memories, movement, and independence—piece by piece. And while science has made incredible strides, one painful truth remains: by the time many of these diseases are recognized, it’s often too late to stop their progress. But what if we could see them coming sooner? What if doctors had tools to catch those earliest, most elusivesigns—before the damage is done? In this work, we offer a step toward that future. We’ve built an AI-driven system that brings together the sharp eye of deep learning and the nuance of clinical insight to help detect neurological diseases at their earliest stages. It looks at more than just images—it reads the story hidden inside MRI and PET scans, and it listens to the patient’s data: their age, symptoms, test scores, even genetic markers. By combining these perspectives, our system paints a fuller, more accurate picture of what’s happening inside the brain. But this isn’t just about making predictions. In healthcare, trust is everything. Doctors and patients deserve to understand why a model reaches a decision—not just what that decision is. So we built explainability right into the system using tools like SHAP and LIME. These show, in clear and human terms, which features tipped the scales—whether it’s a shrinking hippocampus, a declining cognitive score, or a genetic risk factor. We also took care to ensure our model was fair, balanced, and reliable, using techniques that clean the data, balance uneven sample sizes, and fine-tune performance so it works across real-world scenarios. In our early tests on real patient data, this approach has outperformed traditional methods—spotting subtle signs that might otherwise be missed, and doing it with both precision and clarity. At its core, this research is about more than algorithms or models. It’s about giving doctors a trustworthy companion in the fight against neurological decline—and giving patients and families the hope of earlier answers, better treatments, and more time. |
| AUTHOR | Madhurima Patra, Milan Raja, Mangukiya Krutiben Pareshbhai Department of MCA, CMR Institute of Technology, Bengaluru, India |
| PUBLICATION DATE | 2025-09-02 |
| VOLUME | 12 |
| DOI | DOI:10.15680/IJARETY.2025.1204063 |
| 63_AI-Driven Early Diagnosis of Neurological Disorders from Imaging and Clinical Data.pdf | |
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