用CT扫描和深度学习，95%准确率自动判断尸体性别。
Biological Sex Determination in Cadavers Using Deep Learning Algorithms from Computed Tomography Images of Pelvis and Skull
- 用多种深度学习模型分析骨盆与颅骨的二维投影图。
- 整体准确率达95.65%，在有创伤损伤情况下仍稳定有效。
- 适合法医快速鉴定，可减少主观判断误差。
已腐烂尸体的性别鉴定传统依赖视觉人类学分析，面临挑战。本研究评估了包括YOLO26、YOLO11、ConvNeXt-Tiny、EfficientNetV2、ViT-B16、VGG16和ResNet50在内的先进深度学习模型，结合迁移学习，实现从法医计算机断层扫描（CT）图像中自动判定生物性别。分析了来自戈亚尼亚法医医学研究所的141具尸检尸体，涵盖广泛年龄范围及不同保存状况。将骨盆与颅骨的三维重建转化为标准化二维轮廓投影，用于该新技术方法的研究。数据增强弥补样本不足。验证了两种分类场景：二分类（每性别一类）与四分类（每性别的解剖区域各一类）。表现最佳的模型在骨盆区域取得高度一致结果，颅骨区域亦达满意性能，患者级整体准确率为95.65%，召回率92.86%，F1分数94.36%，精确率97.22%，在含创伤伪影病例中仍保持稳定。结果表明该方法技术可行，可提供客观、高速的骨骼分析。由于数据仅来自单一机构和一台CT扫描仪，需在多中心、多设备条件下进一步验证其泛化能力。
原文摘要 · Abstract (English)
Sexual identification of decomposed cadavers challenges traditional methods dependent on visual anthropological analysis. This study evaluates state-of-the-art deep learning (including YOLO26, YOLO11, ConvNeXt-Tiny, EfficientNetV2, ViT-B16, VGG16, and ResNet50) with transfer learning to automatically determine biological sex from forensic computed tomography (CT) scans. We analyzed 141 autopsied cadavers from the Forensic Medical Institute of Goiânia-GO, including a broad age range and varying conditions of preservation. The three-dimensional reconstructions of the pelvis and skull were converted into standardized two-dimensional profile projections, contributing to the study of this new technical approach. Data augmentation techniques compensated for sample limitations. Two scenarios were validated: binary and quaternary classification (one class per sex vs. one class per anatomical region of each sex). The best-performing model achieved highly consistent results on the pelvis region and still satisfactory performance on the skull region, reaching an overall patient-level accuracy of 95.65%, recall of 92.86%, F1- score of 94.36%, and precision of 97.22%, maintaining consistent performance across the evaluated cases, including those with trauma-related artifacts. Results indicate the technical feasibility of the methodology, demonstrating that deep learning models can provide objective, high-speed skeletal analysis. Since the study was conducted using data from a single institution and a single computed tomography scanner, further validation across multiple centers and scanners is required to assess the generalizability of the proposed approach
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