Artificial intelligence and spine imaging: limitations, regulatory issues and future direction
Artificial intelligence and spine imaging: limitations, regulatory issues and future direction
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DOI：
10.1007/s00586-021-07108-4
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发表时间：
2022-01-27
影响因子：
2.8
通讯作者：
Samartzis, Dino
中科院分区：
文献类型：
--
作者：
Hornung, Alexander L.;Hornung, Christopher M.;Samartzis, Dino
背景随着大数据和人工智能(AI)在脊柱护理以及整个医学领域继续走在研究的前沿，有必要仔细考虑所使用的质量和技术。预测建模、数据科学和深度分析占据了中心舞台。在这个领域，人工智能和机器学习(ML)使用脊柱成像的方法在过去十年中得到了相当大的关注。虽然这类应用有几个好处，但也存在局限性，需要考虑。目的：下面的叙述性综述介绍了人工智能，特别是ML在脊柱研究领域的现状，特别是影像研究。方法对截至2021年9月1日的文献进行多数据库评估，讨论人工智能与脊柱成像相关的问题。用英语写的文章被挑选出来，并进行了严格的评估。结果总体而言，综述讨论了ML模型在脊柱成像方面的局限性、数据质量和应用。特别是，我们通过描述广泛可用的成像算法的初步结果来讨论脊柱成像研究中的数据质量和ML算法，该算法目前可供脊柱专家参考，以获得最终可能改变临床决策的脊柱疾病和退行性疾病的严重程度的信息。此外，提高了人们对当前关于执行ML用于脊柱成像的认识不足的法规。结论为脊柱成像提供了高质量、标准化的人工智能应用程序的建议。
Background As big data and artificial intelligence (AI) in spine care, and medicine as a whole, continue to be at the forefront of research, careful consideration to the quality and techniques utilized is necessary. Predictive modeling, data science, and deep analytics have taken center stage. Within that space, AI and machine learning (ML) approaches toward the use of spine imaging have gathered considerable attention in the past decade. Although several benefits of such applications exist, limitations are also present and need to be considered. Purpose The following narrative review presents the current status of AI, in particular, ML, with special regard to imaging studies, in the field of spinal research. Methods A multi-database assessment of the literature was conducted up to September 1, 2021, that addressed AI as it related to imaging of the spine. Articles written in English were selected and critically assessed. Results Overall, the review discussed the limitations, data quality and applications of ML models in the context of spine imaging. In particular, we addressed the data quality and ML algorithms in spine imaging research by describing preliminary results from a widely accessible imaging algorithm that is currently available for spine specialists to reference for information on severity of spine disease and degeneration which ultimately may alter clinical decision-making. In addition, awareness of the current, under-recognized regulation surrounding the execution of ML for spine imaging was raised. Conclusions Recommendations were provided for conducting high-quality, standardized AI applications for spine imaging.