Age Prediction

Age prediction, aiming to estimate a person's age from various biological data, is a rapidly evolving field with applications in healthcare and other domains. Current research focuses on leveraging diverse data modalities, including brain MRI, metabolomics, facial images, voice recordings, and even social media text, employing deep learning architectures like convolutional neural networks (CNNs), graph convolutional networks (GCNs), transformers, and recurrent neural networks (RNNs) such as LSTMs. These models are being refined to improve accuracy, address challenges like data scarcity and variability, and enhance interpretability through techniques like explainable AI (XAI). The ultimate goal is to develop robust and reliable age prediction tools for applications ranging from assessing biological aging and disease risk to improving forensic investigations.