Aug 2026· Zeitschrift für Rechtsmedizin· 0 citations· 30 references
Medicine
TL;DR
Both YOLOv8 and YOLOv11 outperformed YOLOv5 across all metrics, demonstrating superior feature extraction capacity and robust convergence and indicate that next-generation YOLO architectures enable highly accurate and fully automated sex estimation from hand-wrist radiographs without the need for manual annotation or region-of-interest selection.
The proposed multimodal framework provides a reliable approach for sex estimation by combining chest radiographs with structured anatomical annotations and support the development of large-scale national radiographic datasets to facilitate the implementation of artificial intelligence-assisted medico-legal assessments.
K. Kamel, Doaa Tawfik, Nashwa Mohammed Saged et al.· Egyptian Journal of Forensic...· 0 citations
The ensemble estimated adult sex, age, height, and weight from CT-derived DRRs, with generally lower errors in examinations with broader anatomical coverage, with generally lower errors in examinations with broader anatomical coverage.
Tomohiro Kikuchi, Kohei Yamamoto, Y. Nomura et al.· arXiv.org· 0 citations
A foundation model-based approach enables accurate simultaneous estimation of sex and age using femoral CT images and has potential applications in automated forensic biological profiling.
S. Torimitsu, Hisahiro Ikari, Shigeki Tsuneya et al.· Zeitschrift für Rechtsmedizi...· 0 citations
A hierarchical YOLOv8-based framework aligned for comprehensive analysis of panoramic radiographs using structured dental annotations is proposed and enables anatomical localization and integration of detection tasks of panoramic radiographs within a unified pipeline using YOLOv8.
Ho-Ang Tung, Young-Seok Park, Khoa van Pham et al.· Odovtos International Journa...· 0 citations
The adoption of XAI frameworks is expected to enhance transparency, accountability, and forensic reliability, thereby enabling AI-based systems to become scientifically robust and legally valid tools in forensic odontology.
B. Çarıkçıoğlu· Turkish Journal of Forensic...· 0 citations
A two-stage deep learning model designed for the detection of landmarks in hip X-ray images, aiming to improve the accuracy of deformity quantification, demonstrated a brief learning time due to the small image size loaded in both steps, while also offering high precision through the use of a bounding box.
Mahdie Aghasizade, M. Karimpour, Masoud Shariat Panahi et al.· The Archives of Bone & Joint...· 0 citations
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