Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Int J Environ Res Public Health. 2022 Sep 16;19(18):11708. doi: 10.3390/ijerph191811708.
Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.
脊柱疾病是全球范围内最常见的疼痛和残疾原因之一。影像学检查在脊柱护理中是一种重要的诊断程序。影像学检查可以提供通过普通目视检查无法获得的信息和见解。多尺度体内询问有可能通过成像、人工智能 (AI) 和放射组学技术的融合来改善对病变的评估和监测。AI 正在彻底改变计算机视觉、自动驾驶、自然语言处理和语音识别。这些革命性技术已经在影响放射学、诊断学和其他领域,在这些领域中,自动化解决方案可以提高精度和可重复性。在这篇叙述性综述的第一部分,我们简要解释了目前正在开发的许多方法,特别强调了那些在脊柱成像研究中使用的方法。然后详细介绍了 AI 以前在涉及脊柱成像的挑战中的应用,包括成像适宜性和方案制定、图像采集和重建、图像呈现、图像解释和定量图像分析。最后,讨论了 AI 在脊柱成像中的未来应用。AI 有可能极大地影响脊柱成像的每一个步骤。通过提高图像质量、成像效率和诊断准确性,AI 可以使脊柱的图像对患者和医生更有用。