Welcome to our research page on the role of Artificial Intelligence (AI) in revolutionizing medical diagnosis. Specifically, we are exploring how AI models, like EfficientNet and Convolutional Neural Networks (CNNs), can be applied to the detection of osteoporosis using facial panoramic radiography images.
This groundbreaking research is being conducted in collaboration with Dr. Farias Mylene at Texas State University, with the potential to make significant strides in medical imaging and diagnostics.
📊 Medical Diagnosis Research
Focus:
We are focusing on osteoporosis screening using EfficientNet and CNN models applied to complete and cropped facial panoramic radiography images. This innovative method aims to improve diagnostic accuracy, reduce the time required for diagnosis, and make osteoporosis detection more accessible.
Tech Stack:
Machine Learning: We are using machine learning models like EfficientNet for image classification.
Convolutional Neural Networks (CNNs): CNNs are employed for their ability to detect complex features in medical imaging.
Medical Imaging: The application of AI in analyzing radiographic images to assist in diagnosing osteoporosis.
Current Progress:
This research is ongoing with promising results, and we believe it could revolutionize the way osteoporosis is diagnosed in clinical settings.
🔬 Future Implications
AI-based diagnosis, such as this project, has the potential to make significant improvements in the healthcare sector. By integrating AI into diagnostic tools, we can provide more accurate and timely results, leading to better treatment options for patients. In the future, this type of technology could be applied to a wider range of diseases and imaging methods, paving the way for smarter healthcare systems.