Machine learning and facial recognition for down syndrome detection: A comprehensive review

Abstract

This review article examines advancements in automated facial recognition methods for diagnosing Down syndrome in children, focusing on the integration of machine learning (ML) and deep learning (DL) strategies. Traditionally diagnosed through clinical assessments, Down syndrome, a genetic disorder characterized by distinctive facial features, has benefited from recent advancements in computer vision and artificial intelligence (AI). This paper explores various facial analysis techniques, including deep convolutional neural networks (DCNNs) and hybrid models combining traditional image processing with deep learning. The review highlights the strengths and limitations of these methods, the importance of large and diverse datasets, and the need for models capable of handling variations in lighting, facial angles, and genetic diversity. Additionally, ethical considerations related to privacy, bias, and data diversity are discussed to emphasize the challenges of implementing these technologies in clinical practice. The findings suggest that while AI-driven facial recognition systems hold promise in enhancing diagnostic accuracy, they must be complemented with traditional clinical methods and improved datasets to ensure reliable and equitable healthcare outcomes.