Chang Yu-Cherng, Del Toro Cinthia, Gjolaj Joseph P, Braga Thiago A, Subhawong Ty K
Department of Radiology, University of Miami Miller School of Medicine/Jackson Memorial Hospital, Miami, FL 33136, United States.
University of Miami Miller School of Medicine, Miami, FL 33136, United States.
N Am Spine Soc J. 2025 May 27;23:100621. doi: 10.1016/j.xnsj.2025.100621. eCollection 2025 Sep.
As artificial intelligence (AI) increases its footprint in spine imaging, gauging the clinical relevance of new developments poses an increasingly difficult challenge, especially given the majority of developments reflect experimental or early work. With this summary of available AI tools, focusing on those in clinical use, the benefits of AI in spine imaging are explained for radiologists and surgeons to understand the current state of and potentially the decision to adopt AI in clinical practice.
Through a narrative review of publications relating to "artificial intelligence" and "spine imaging" in the PubMed database, this article provides an update on AI applications in spine imaging being utilized in current clinical practice.
Current applications of AI in spine imaging range from deep learning image reconstruction and denoising, spine segmentation and biometry, radiological report generation, surgical outcomes prediction, surgical planning, to intraoperative assistance. Developments in deep learning reconstruction (DLR) are most mature and demonstrate improvements to imaging speed and interpretability compared to non-AI alternatives. While clinical implementations exist in other use cases, their performance remains either an area of active investigation or comparable to the level of a human.
Uses of AI in spine imaging span multiple applications with early clinical implementation in most areas, suggesting a promising future ahead.
随着人工智能（AI）在脊柱成像领域的影响力不断扩大，评估新进展的临床相关性面临着日益艰巨的挑战，尤其是考虑到大多数进展都反映了实验性或早期工作。通过对现有AI工具的总结，重点关注临床应用中的工具，向放射科医生和外科医生解释了AI在脊柱成像中的益处，以帮助他们了解当前的状况以及在临床实践中采用AI的潜在决策。
通过对PubMed数据库中与“人工智能”和“脊柱成像”相关的出版物进行叙述性综述，本文提供了当前临床实践中正在使用的AI在脊柱成像中的应用最新情况。
目前AI在脊柱成像中的应用范围涵盖深度学习图像重建与去噪、脊柱分割与生物测量、放射学报告生成、手术结果预测、手术规划以及术中辅助。深度学习重建（DLR）的发展最为成熟，与非AI替代方法相比，在成像速度和可解释性方面都有改进。虽然在其他用例中也有临床应用，但它们的性能仍处于积极研究阶段或与人类水平相当。
AI在脊柱成像中的应用涵盖多个领域，且在大多数领域已早期应用于临床，预示着未来前景广阔。
