{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T22:00:12Z","timestamp":1793311212421,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal Orthopaedic Research"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long\u2010term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X\u2010ray and magnetic resonance imaging (MRI) are standard imaging methods for staging ONFH, MRI can be costly and time\u2010consuming. The research focuses on utilizing artificial intelligence (AI) to enhance the evaluation of radiographic images for ONFH detection. The study involved analyzing X\u2010ray and MRI from 102 control hips and 104 ONFH\u2010affected 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\u2010recall curve. Additionally, AI generated synthetic MRI (sMRI) from X\u2010ray 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\u2010generated 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.<\/jats:p>","DOI":"10.1002\/jor.26026","type":"journal-article","created":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T06:39:06Z","timestamp":1732343946000},"page":"650-659","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1\u2010weighted MRI generation from radiographs"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4122-7119","authenticated-orcid":false,"given":"Issei","family":"Shinohara","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery Stanford University School of Medicine Stanford California USA"},{"name":"Department of Orthopaedic Surgery Kobe 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