Shinohara Issei, Inui Atsuyuki, Hwang Katherine, Murayama Masatoshi, Susuki Yosuke, Uno Tomohiro, Gao Qi, Morita Mayu, Chow Simon Kwoon-Ho, Tsubosaka Masanori, Mifune Yutaka, Matsumoto Tomoyuki, Kuroda Ryosuke, Goodman Stuart B
Department of Orthopaedic Surgery, Stanford University School of Medicine, Stanford, California, USA.
Department of Orthopaedic Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
J Orthop Res. 2025 Mar;43(3):650-659. doi: 10.1002/jor.26026. Epub 2024 Nov 23.
This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long-term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X-ray and magnetic resonance imaging (MRI) are standard imaging methods for staging ONFH, MRI can be costly and time-consuming. The research focuses on utilizing artificial intelligence (AI) to enhance the evaluation of radiographic images for ONFH detection. The study involved analyzing X-ray and MRI from 102 control hips and 104 ONFH-affected hips at Association Research Circulation Osseous (ARCO) Stage II and IIIa. We employed transfer learning with the YOLOv8 model for object detection, using 80% of the data for training and 20% for validation, then assessed detection accuracy through mean average precision (mAP) and a precision-recall curve. Additionally, AI generated synthetic MRI (sMRI) from X-ray images using a Generative Adversarial Network (GAN) and evaluated their similarity to original MRI. Results showed that the mAP for ONFH detection was 0.923 for the YOLOv8n model and 0.951 for YOLOv8x. The GAN-generated sMRI exhibited lower image quality compared with originals but maintained potential for lesion assessment. Intrarater reliability among evaluators was high. The findings indicate that AI techniques, particularly YOLOv8 for object detection and GAN for image generation, can effectively assist in ONFH screening, despite some limitations in the generated MRI quality.
本研究强调了在接受长期糖皮质激素治疗的年轻患者中早期检测股骨头坏死（ONFH）的重要性，这些患者包括急性淋巴细胞白血病、狼疮及其他诊断的患者。虽然X线和磁共振成像（MRI）是ONFH分期的标准成像方法，但MRI成本高且耗时。该研究聚焦于利用人工智能（AI）增强对用于ONFH检测的X线图像的评估。该研究涉及分析来自骨循环研究协会（ARCO）II期和IIIa期的102个对照髋关节和104个受ONFH影响的髋关节的X线和MRI。我们采用基于YOLOv8模型的迁移学习进行目标检测，使用80%的数据进行训练，20%的数据进行验证，然后通过平均精度均值（mAP）和精确率-召回率曲线评估检测准确性。此外，AI使用生成对抗网络（GAN）从X线图像生成合成MRI（sMRI），并评估其与原始MRI的相似性。结果显示，YOLOv8n模型检测ONFH的mAP为0.923，YOLOv8x为0.951。与原始MRI相比，GAN生成的sMRI图像质量较低，但仍保持了病变评估的潜力。评估者之间的组内可靠性较高。研究结果表明，AI技术，特别是用于目标检测的YOLOv8和用于图像生成的GAN，尽管生成的MRI质量存在一些局限性，但仍可有效辅助ONFH筛查。
