Shimizu Hirokazu, Enda Ken, Koyano Hidenori, Shimizu Tomohiro, Shimodan Shun, Sato Komei, Ogawa Takuya, Tanaka Shinya, Iwasaki Norimasa, Takahashi Daisuke
Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
Department of Cancer Pathology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
Sci Rep. 2024 Aug 1;14(1):17826. doi: 10.1038/s41598-024-68484-7.
Bimodal convolutional neural networks (CNNs) are frequently combined with patient information or several medical images to enhance the diagnostic performance. However, the technologies that integrate automatically generated clinical measurements within the images are scarce. Hence, we developed a bimodal model that produced automatic algorithm for clinical measurement (aaCM) from radiographic images and integrated the model with CNNs. In this multicenter research project, the diagnostic performance of the model was investigated with 813 radiographic hip images of infants at risk of developmental dysplasia of the hips (232 and 581 images of unstable and stable hips, respectively), with the ground truth defined by provocative examinations. The results indicated that the accuracy of aaCM was equal or higher than that of specialists, and the bimodal model showed better diagnostic performance than LightGBM, XGBoost, SVM, and single CNN models. aaCM can provide expert's knowledge in a high level, and our proposed bimodal model has better performance than the state-of-art models.
双峰卷积神经网络（CNN）经常与患者信息或多幅医学图像相结合，以提高诊断性能。然而，将自动生成的临床测量值整合到图像中的技术却很少。因此，我们开发了一种双峰模型，该模型可从X线图像中生成临床测量自动算法（aaCM），并将该模型与CNN集成。在这个多中心研究项目中，我们用813张有发育性髋关节发育不良风险的婴儿的X线髋关节图像（分别有232张不稳定髋关节图像和581张稳定髋关节图像）对该模型的诊断性能进行了研究，其真实情况由激发试验确定。结果表明，aaCM的准确性等于或高于专家，并且双峰模型的诊断性能优于LightGBM、XGBoost、支持向量机（SVM）和单CNN模型。aaCM可以高水平地提供专家知识，并且我们提出的双峰模型比现有模型具有更好的性能。
