At just 12 years old, Nag Shivani Puram has already achieved something remarkable—she’s developed an Artificial intelligence (AI) project aimed at detecting the early onset of Alzheimer’s disease and brain tumors. As a junior in high school already, Shivani’s work is not only a testament to her talent but also her commitment to making a meaningful impact in healthcare.
The Inspiration Behind the Project
Shivani’s journey into the world of Artificial intelligence and healthcare began with a deep curiosity about the human brain and a desire to use technology for good. She was particularly struck by the challenges surrounding the early detection of neurodegenerative diseases like Alzheimer’s, which affects millions worldwide, and brain tumors, which are notoriously difficult to diagnose in their initial stages. The earlier these conditions are detected, the better the chances of successful treatment, making early diagnosis a crucial goal.
Seeing the potential of AI to revolutionize healthcare, Shivani decided to take on the challenge of developing a model that could assist in the early detection of these diseases. Her project is the culmination of months of research, coding, and testing, guided by a passion for using technology to solve real-world problems.
The AI Project: An Overview
Shivani’s project leverages cutting-edge machine learning techniques, particularly deep learning, to analyze medical imaging data for early signs of Alzheimer’s disease and brain tumors. The project is based on the integration of two powerful types of neural networks: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Convolutional Neural Networks (CNNs): CNNs are a type of deep learning model particularly well-suited for analyzing visual data. They are widely used in image recognition tasks because they excel at detecting patterns and features in images. In Shivani’s project, CNNs process MRI scans, searching for patterns that may indicate the presence of early-stage Alzheimer’s disease or brain tumors. The CNNs help the model identify subtle differences in the brain’s structure and function that might be too minute for human eyes to detect.
Recurrent Neural Networks (RNNs): While CNNs are adept at analyzing spatial data, RNNs are designed to handle sequential data, making them ideal for tracking changes over time. Shivani integrated RNNs into her project to analyze sequential MRI scans of patients. By evaluating how the brain changes across multiple scans, the model can better predict the onset of these conditions. This is particularly useful for Alzheimer’s disease, where changes in the brain happen gradually over time.
Future Directions
Looking ahead, Shivani plans to continue refining her model and exploring additional avenues for its application with her team. She is particularly interested in integrating multimodal data, such as combining MRI and PET scans with genetic information, to improve the model’s predictive power. She also hopes to collaborate with medical professionals to test her model in clinical settings, with the ultimate goal of making her tool available for use in hospitals and clinics.
Additionally, Shivani is eager to share her work with the broader AI and medical communities. She has already published a research paper detailing her methodology and findings in an international journal, and she plans to submit to conferences and journals.
