Abstract：
Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.
摘要：
人工智能（AI）正在通过自动分析和增强决策来改变脊柱成像和患者护理。这篇综述提出了一种基于临床任务的评估，强调人工智能技术对脊柱成像和患者护理不同方面的具体影响。我们首先讨论AI如何通过去噪或伪影减少等技术来提高图像质量。然后，我们探索AI如何实现解剖测量的有效量化，脊柱曲率参数，椎骨分割，和光盘分级。这有助于客观，准确的解释和诊断。AI模型现在可以可靠地检测关键的脊柱病变，在识别裂缝等任务中实现专家级的表现，狭窄,感染,和肿瘤。除了诊断，AI还通过合成计算机断层扫描生成来协助手术计划，增强现实系统，和机器人引导。此外，AI图像分析与临床数据相结合，可实现个性化预测，以指导治疗决策，例如预测脊柱手术结果。然而,在临床上实施人工智能仍然需要解决挑战，包括模型可解释性，概括性，和数据限制。多中心协作使用大型，不同的数据集对进一步推进该领域至关重要。虽然采用障碍仍然存在，AI为脊柱成像工作流程带来了革命性的变革机会，使临床医生能够将数据转化为可操作的见解，以改善患者护理。
