Accurate and real-time polyp segmentation plays a vital role in the early detection of colorectal cancer. However, existing methods often rely on computationally expensive backbones, single attention mechanisms, and suboptimal feature fusion strategies, limiting their practicality in real-world scenarios. In this work, we propose a lightweight yet effective deep learning framework that strikes a balance between precision and efficiency through a carefully designed architecture. Specifically, we adopt a MobileNetV4-based hybrid backbone to extract rich multi-scale features with significantly fewer parameters than conventional backbones, making the model well-suited for resource-constrained cl... More >

This systematic review and meta-analysis assesses the transformative effect of artificial intelligence (AI) on forensic odontology, concentrating on gains in identification accuracy and workflow efficiency. Traditionally, human identification in this specialty depends on meticulous comparison of dental charts and radiographs. The integration of AI-driven technologies—including machine-learning algorithms and image-recognition networks—has begun to expedite core tasks such as bite-mark interpretation, dental-age estimation and record reconciliation, while also limiting examiner bias and clerical error. Following PRISMA guidelines to ensure methodological rigour, we searched PubMed, Scienc... More >