Age estimation using medial clavicle by histomorphometry method with artificial intelligence: A review.

Abstract

This review research critically assesses the evolving landscape of age estimation methodologies, with a particular focus on the innovative integration of histomorphometry and artificial intelligence (AI) in the analysis of the medial clavicle. The medial clavicle emerges as a crucial skeletal feature for predicting age, offering valuable insights into the morphological changes occurring throughout an individual's lifespan. Through an in-depth exploration of histological complexities, including variations in osteons, trabecular structures, and cortical thickness, this review elucidates their utility as viable indicators for age-related evaluations. This framework is augmented by the incorporation of AI technology, which enables automatic picture identification, feature extraction, and complicated pattern analysis. Our review of previous research highlights the promise of AI in improving prediction models for nuanced age estimates, highlighting the importance of large-scale, diversified datasets and thorough cross-validation. This thorough study, which addresses ethical concerns as well as the influence of population-specific characteristics, moves the debate around age estimate ahead, presenting insights with consequences for forensic anthropology, clinical diagnoses, and future research avenues.