Harada Garrett K, Siyaji Zakariah K, Younis Sadaf, Louie Philip K, Samartzis Dino, An Howard S
Department of Orthopaedic Surgery, Division of Spine Surgery, Rush University Medical Center, Chicago, USA.
International Spine Research and Innovation Initiative, Rush University Medical Center, Chicago, USA.
Spine Surg Relat Res. 2019 Nov 1;4(2):99-110. doi: 10.22603/ssrr.2020-0011. eCollection 2020.
To review and highlight the historical and recent advances of imaging in spine surgery and to discuss current applications and future directions.
A PubMed review of the current literature was performed on all relevant articles that examined historical and recent imaging techniques used in spine surgery. Studies were examined for their thoroughness in description of various modalities and applications in current and future management.
We reviewed 97 articles that discussed past, present, and future applications for imaging in spine surgery. Although most historical approaches relied heavily upon basic radiography, more recent advances have begun to expand upon advanced modalities, including the integration of more sophisticated equipment and artificial intelligence.
Since the days of conventional radiography, various modalities have emerged and become integral components of the spinal surgeon's diagnostic armamentarium. As such, it behooves the practitioner to remain informed on the current trends and potential developments in spinal imaging, as rapid adoption and interpretation of new techniques may make significant differences in patient management and outcomes. Future directions will likely become increasingly sophisticated as the implementation of machine learning, and artificial intelligence has become more commonplace in clinical practice.
回顾并强调脊柱外科影像学的历史及近期进展,探讨其当前应用及未来方向。
对PubMed上所有相关文章进行综述,这些文章研究了脊柱外科中使用的历史及近期影像学技术。考察这些研究对各种模式的描述以及在当前和未来管理中的应用是否全面。
我们回顾了97篇讨论脊柱外科影像学过去、现在和未来应用的文章。尽管大多数历史方法严重依赖基本的X线摄影,但近期进展已开始扩展到先进模式,包括更复杂设备和人工智能的整合。
自传统X线摄影时代以来,各种模式不断涌现,并成为脊柱外科医生诊断工具的重要组成部分。因此,从业者有必要了解脊柱影像学的当前趋势和潜在发展,因为新技术的快速采用和解读可能会对患者管理和治疗结果产生重大影响。随着机器学习和人工智能在临床实践中的应用越来越普遍,未来的方向可能会变得越来越复杂。