Cavlak Nevin, Çınarer Gökalp, Erkoç Mustafa Fatih, Kılıç Kazım
Department of Forensic Medicine, Faculty of Medicine, Yozgat Bozok University, Yozgat, Türkiye.
Department of Computer Engineering, Faculty of Engineering-Architecture, Yozgat Bozok University, Yozgat, 66900, Türkiye.
Forensic Sci Med Pathol. 2025 Feb 19. doi: 10.1007/s12024-025-00943-7.
Conducting sex estimation based on bones through morphometric methods increases the need for automatic image analyses, as doing so requires experienced staff and is a time-consuming process. In this study, sex estimation was performed with the EfficientNetB3, MobileNetV2, Visual Geometry Group 16 (VGG16), ResNet50, and DenseNet121 architectures on patellar magnetic resonance images via a developed model. Within the scope of the study, 6710 magnetic resonance sagittal patella image slices of 696 patients (293 males and 403 females) were obtained. The performance of artificial intelligence algorithms was examined through deep learning architectures and the developed classification model. Considering the performance evaluation criteria, the best accuracy result of 88.88% was obtained with the ResNet50 model. In addition, the proposed model was among the best-performing models with an accuracy of 85.70%. When all these results were examined, it was concluded that positive sex estimation results could be obtained from patella magnetic resonance image (MRI) slices without the use of the morphometric method.
通过形态测量方法基于骨骼进行性别估计增加了对自动图像分析的需求，因为这样做需要经验丰富的工作人员且是一个耗时的过程。在本研究中，通过一个开发的模型，利用高效神经网络B3（EfficientNetB3）、移动神经网络V2（MobileNetV2）、视觉几何组16（VGG16）、残差神经网络50（ResNet50）和密集连接神经网络121（DenseNet121）架构对髌骨磁共振图像进行性别估计。在研究范围内，获取了696例患者（293例男性和403例女性）的6710张髌骨磁共振矢状位图像切片。通过深度学习架构和开发的分类模型检查人工智能算法的性能。考虑性能评估标准，ResNet50模型获得了88.88%的最佳准确率结果。此外，所提出的模型也是性能最佳的模型之一，准确率为85.70%。当检查所有这些结果时，得出结论：无需使用形态测量方法即可从髌骨磁共振成像（MRI）切片中获得阳性性别估计结果。
