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A Review on the Use of Artificial Intelligence in Spinal Diseases

机译:脊髓疾病中人工智能使用的综述

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Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.
机译:人工神经网络(ANNS)已在各种现实世界应用中使用,并作为各种医学分支的有希望的领域。该审查旨在确定Anns在脊柱疾病中的作用。从1993年到2020年的Scopus和Medline的电子数据库搜查了文学,英语出版物报告了Anns在脊柱疾病中的应用。搜索策略被设定为以下关键词的组合:“人工神经网络”,“脊柱”,“背部疼痛,”“预后”“分级”,“分类”,“预测”“分割”,“分割,”生物力学,“”深入学习“和”成像“。总结了包括研究的主要结果,重点是脊柱疾病最近的进展及其在诊断和预后程序中的应用。根据搜索策略,从Medline和Scopus数据库中检索了一组3,653篇文章。经过仔细评估摘要后,进一步审查了89份合格文件的全文,其中79篇文章满足了本综述的纳入标准。我们的审查表明,在脊柱疾病的管理中,包括(1)脊柱疼痛患者患者诊断和评估脊柱疾病的诊断和评估脊柱手术后的诊断和评估; (2)从放射线图像中提取的临床相关信息的增强,以预测PFIRRMANN等级,修饰变化和脊柱狭窄等级在磁共振图像上自动上限; (3)腰椎脊柱狭窄,腰椎间盘突出症和患者报告的结果预测腰椎融合手术,术前规划和术中援助; (4)其在脊柱疾病的生物力学评估中的应用。证据表明,ANNS可以成功地用于优化脊柱疾病的诊断,预后和结果预测。因此,将ANNS纳入脊柱临床实践可能改善临床决策。

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