Oura Petteri, Junno Juho-Antti, Hunt David, Lehenkari Petri, Tuukkanen Juha, Maijanen Heli
Department of Forensic Medicine, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Forensic Medicine Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland; Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland; Cancer and Translational Medicine Research Unit, Faculty of Medicine, University of Oulu, Oulu, Finland; Department of Archaeology, Faculty of Humanities, University of Oulu, Oulu, Finland; Archaeology, Faculty of Arts, University of Helsinki, Helsinki, Finland.
Leg Med (Tokyo). 2023 Mar;61:102211. doi: 10.1016/j.legalmed.2023.102211. Epub 2023 Jan 31.
Although knee measurements yield high classification rates in metric sex estimation, there is a paucity of studies exploring the knee in artificial intelligence-based sexing. This proof-of-concept study aimed to develop deep learning algorithms for sex estimation from radiographs of reconstructed cadaver knee joints belonging to the Terry Anatomical Collection. A total of 199 knee radiographs were obtained from 100 skeletons (46 male and 54 female cadavers; mean age at death 64.2 years, range 50-102 years) whose tibiofemoral joints were reconstructed in standard anatomical position. The AIDeveloper software was used to train, validate, and test neural network architectures in sex estimation based on image classification. Of the explored algorithms, an MhNet-based model reached the highest overall testing accuracy of 90.3%. The model was able to classify all females (100.0%) and most males (78.6%) correctly. These preliminary findings encourage further research on artificial intelligence-based methods in sex estimation from the knee joint. Combining radiographic data with automated and externally validated algorithms may establish valuable tools to be utilized in forensic anthropology.
尽管膝关节测量在性别估计的指标分类中具有较高的准确率，但在基于人工智能的性别鉴定中，对膝关节的研究却很少。这项概念验证研究旨在开发深度学习算法，用于从属于特里解剖学收藏的重建尸体膝关节X线片中进行性别估计。从100具骨骼（46具男性和54具女性尸体；平均死亡年龄64.2岁，范围50-102岁）中获得了总共199张膝关节X线片，这些尸体的胫股关节在标准解剖位置进行了重建。使用AIDeveloper软件基于图像分类对神经网络架构进行性别估计的训练、验证和测试。在探索的算法中，基于MhNet的模型达到了最高的总体测试准确率90.3%。该模型能够正确分类所有女性（100.0%）和大多数男性（78.6%）。这些初步发现鼓励进一步研究基于人工智能的膝关节性别估计方法。将X线数据与经过自动和外部验证的算法相结合，可能会建立有价值的工具，用于法医人类学。
