Xu Junlin, Wen Xiaobo, Shao Yingchun, Liu Qing, Zhou Sha, Jiyixuan Li, Wang Dan, Yang Ying, Li Han, Xue Linyuan, Xing Kunyue, Wu Xiaolin, Xing Dongming
The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao, 266071, China.
Qingdao Cancer Institute, Qingdao University, Qingdao, 266071, China.
Radiol Med. 2025 Aug 7. doi: 10.1007/s11547-025-02051-6.
Fractures and their complications are recognized as major public health problems. Especially for occult fractures that are difficult to judge radiologically, timely and accurate diagnosis is particularly important for the treatment and prognosis of patients. In recent years, the successful application of radiomics and deep learning in medical diagnosis has shown great potential for providing more timely and accurate diagnostic methods for occult fractures. This review provides an introduction to radiomics and deep learning, summarizes their respective characteristics in detecting occult fractures, and subsequently conducts a detailed analysis on the potential value and future prospects of integrating these two techniques to develop an enhanced approach for prompt and precise detection of occult fractures.
骨折及其并发症被认为是重大的公共卫生问题。尤其是对于放射学上难以判断的隐匿性骨折，及时、准确的诊断对患者的治疗和预后尤为重要。近年来，放射组学和深度学习在医学诊断中的成功应用显示出为隐匿性骨折提供更及时、准确诊断方法的巨大潜力。本文综述介绍了放射组学和深度学习，总结了它们在检测隐匿性骨折方面各自的特点，随后对整合这两种技术以开发一种增强方法来快速、精确检测隐匿性骨折的潜在价值和未来前景进行了详细分析。
