PDF(Pubmed)
Abstract：
BACKGROUND: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.
OBJECTIVE: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.
METHODS: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.
RESULTS: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.
CONCLUSIONS: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.
BACKGROUND: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.
OBJECTIVE: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.
METHODS: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.
RESULTS: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.
CONCLUSIONS: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.
BACKGROUND: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv.
摘要：
背景：瀑布及其后果是世界范围内严重的公共卫生问题。每一年,发生3730万起需要医疗护理的跌倒。因此,跌倒风险的分析对于预防非常重要。人工智能（AI）是一种创新工具，用于通过数据分析创建跌倒风险的预测统计模型。
目的：本综述的目的是分析AI在分析与姿势控制和跌倒风险相关的数据方面的应用的现有证据。
方法：在6个数据库中进行了文献检索，纳入标准如下：文章必须在过去5年内发表（从2018年到2024年），他们不得不应用一些人工智能方法，人工智能分析必须应用于由人类组成的样本数据，分析的样本必须由独立行走的个体组成，有或没有外部矫形装置的帮助。
结果：我们共获得3858篇文章，其中22人最终被选中。在不同的研究中，用于后续分析的数据提取有所不同：其中82％（18/22）通过测试或功能评估提取数据，其余18％（4/22）通过现有病历提取。在整个文章中使用了不同的AI技术。评论中包含的所有研究在通过AI获得的预测模型中获得了>70％的准确性值。
结论：使用人工智能被证明是创建跌倒风险预测模型的有价值的工具。使用该工具可能会产生重大的社会经济影响，因为它可以开发具有高精度的低成本预测模型。
背景：PROSPEROCRD42023443277;https://tinyurl.com/4sb72ssv.
目的：本综述的目的是分析AI在分析与姿势控制和跌倒风险相关的数据方面的应用的现有证据。
方法：在6个数据库中进行了文献检索，纳入标准如下：文章必须在过去5年内发表（从2018年到2024年），他们不得不应用一些人工智能方法，人工智能分析必须应用于由人类组成的样本数据，分析的样本必须由独立行走的个体组成，有或没有外部矫形装置的帮助。
结果：我们共获得3858篇文章，其中22人最终被选中。在不同的研究中，用于后续分析的数据提取有所不同：其中82％（18/22）通过测试或功能评估提取数据，其余18％（4/22）通过现有病历提取。在整个文章中使用了不同的AI技术。评论中包含的所有研究在通过AI获得的预测模型中获得了>70％的准确性值。
结论：使用人工智能被证明是创建跌倒风险预测模型的有价值的工具。使用该工具可能会产生重大的社会经济影响，因为它可以开发具有高精度的低成本预测模型。
背景：PROSPEROCRD42023443277;https://tinyurl.com/4sb72ssv.
