骨科手术中的人工智能与机器学习：一项系统综述方案
Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol.
作者信息
Maffulli Nicola, Rodriguez Hugo C, Stone Ian W, Nam Andrew, Song Albert, Gupta Manu, Alvarado Rebecca, Ramon David, Gupta Ashim
机构信息
Department of Musculoskeletal Disorders, School of Medicine and Surgery, University of Salerno, Fisciano, Italy.
San Giovanni di Dio e Ruggi D'Aragona Hospital "Clinica Orthopedica" Department, Hospital of Salerno, Salerno, Italy.
出版信息
J Orthop Surg Res. 2020 Oct 19;15(1):478. doi: 10.1186/s13018-020-02002-z.
BACKGROUND
Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery.
METHODS
A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis.
DISCUSSION
Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed.
TRIAL REGISTRATION
The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.
背景
人工智能（AI）和机器学习（ML）已融入我们的日常生活，并在医学的一些主要领域，如心脏病学和放射学中得到了极大的发展。虽然这些专业迅速接受了AI和ML，但骨科手术在这方面的进展较慢。幸运的是，最近新的研究激增，强调了进行系统综述的必要性。本系统综述的主要目的是提供骨科手术领域AI和ML进展的最新情况。次要目的将是评估AI和ML在骨科手术中提供临床诊断以及预测术后结果和并发症方面的应用。
方法
将在PubMed、ScienceDirect和谷歌学术数据库中进行系统检索，查找截至2020年9月发表的英文、意大利文、法文、西班牙文和葡萄牙文文章。参考文献将由至少两名独立评审员根据PRISMA指南进行筛选和评估以确定其是否符合要求。研究必须适用于骨科干预以及急慢性骨科肌肉骨骼损伤才被视为符合要求。如果是动物研究且与骨科干预无关，或者未产生临床数据，则将研究排除。将比较使用和不使用ML算法时获得临床诊断和预测术后结果的金标准流程和做法。将纳入任何评估骨科手术术后结果预测率或诊断能力的病例报告和其他初步研究。将审查系统综述或文献综述以确定进一步纳入的研究，荟萃分析的结果将不包括在分析中。
讨论
我们的研究结果将评估AI和ML在骨科手术领域的进展。我们预计会发现大量未加控制的研究以及一小部分描述临床护理实际应用和结果的文章。可能需要队列研究和大型随机对照试验。
试验注册
该方案将在开始前在PROSPERO国际系统综述前瞻性注册库中注册。
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