Early Stage

Early detection of various conditions is a major focus of current research, aiming to improve diagnosis, prognosis, and treatment outcomes. This involves applying machine learning, particularly deep neural networks like convolutional neural networks (CNNs) and support vector machines (SVMs), to diverse data types including medical images (e.g., MRI, CT scans), physiological signals, and even environmental cues like door slamming sounds. These models are being used to identify subtle indicators of diseases such as Alzheimer's, breast cancer, and chronic kidney disease, as well as developmental conditions like torticollis. Successful implementation hinges on addressing challenges like data scarcity, model interpretability, and ensuring robust performance across different datasets and clinical settings.