Yuji Zhang , Ming Ma , Cong Tian , Jinmin Liu , Xingchun Huang , Zhenkun Duan , Xianxu Zhang , Song Sun , Qiang Zhang , Bin Geng
{"title":"骨质疏松症筛查工具的现状与困境:叙述性综述","authors":"Yuji Zhang , Ming Ma , Cong Tian , Jinmin Liu , Xingchun Huang , Zhenkun Duan , Xianxu Zhang , Song Sun , Qiang Zhang , Bin Geng","doi":"10.1016/j.clnesp.2024.10.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, in addition to exploring the potential of AI-integrated X-ray imaging in osteoporosis screening, especially its ability to improve accuracy and applicability to different populations. To break through the dilemma of low accessibility, poor clinical translation, complexity of use, and apparent limitations of screening results of existing osteoporosis screening tools.</div></div><div><h3>Data sources</h3><div>A comprehensive literature search was performed using PubMed, Web of Science, and CNKI databases. The search included articles published between 2000 and 2023, focusing on studies evaluating osteoporosis screening tools, Artificial intelligence applications in medical imaging, and implementing AI technologies in clinical settings.</div></div><div><h3>Study selection</h3><div>The Osteoporosis Risk Assessment Tool for Asians (OSTA), the Simple Calculated Osteoporosis Risk Estimator (SCORE), age, body size, one or no estrogen ever (ABONE), and the Osteoporosis Risk Index (OSIRIS) are the six commonly used screening tools for osteoporosis that are discussed in this review. In addition, the performance of AI-integrated imaging systems is explored in light of relevant research advances in Artificial intelligence in osteoporosis screening. Studies of the use of these tools in different populations and their advantages and disadvantages were included in the selection criteria.</div></div><div><h3>Results</h3><div>The results highlight that AI-integrated X-ray imaging technologies offer significant improvements over traditional osteoporosis screening tools. Artificial intelligence systems demonstrated higher accuracy by incorporating complex clinical data and providing personalized assessments for diverse populations. The studies showed that AI-driven imaging could enhance sensitivity and specificity, particularly in detecting early-stage bone density loss in patients with complex clinical profiles. The findings also suggest that Artificial intelligence technologies have the potential to be effectively applied in resource-limited settings through the use of mobile devices and remote diagnostics.</div></div><div><h3>Conclusions</h3><div>AI-integrated X-ray imaging technology significantly advances osteoporosis screening, offering more accurate and adaptable solutions than traditional tools. Its ability to incorporate complex clinical data and apply it across various demographic groups makes it particularly promising in diverse and resource-limited environments. Further research is needed to explore the full potential of AI in enhancing screening accessibility and effectiveness, particularly in underserved populations.</div></div>","PeriodicalId":10352,"journal":{"name":"Clinical nutrition ESPEN","volume":"64 ","pages":"Pages 207-214"},"PeriodicalIF":2.9000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Current status and dilemmas of osteoporosis screening tools: A narrative review\",\"authors\":\"Yuji Zhang , Ming Ma , Cong Tian , Jinmin Liu , Xingchun Huang , Zhenkun Duan , Xianxu Zhang , Song Sun , Qiang Zhang , Bin Geng\",\"doi\":\"10.1016/j.clnesp.2024.10.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, in addition to exploring the potential of AI-integrated X-ray imaging in osteoporosis screening, especially its ability to improve accuracy and applicability to different populations. To break through the dilemma of low accessibility, poor clinical translation, complexity of use, and apparent limitations of screening results of existing osteoporosis screening tools.</div></div><div><h3>Data sources</h3><div>A comprehensive literature search was performed using PubMed, Web of Science, and CNKI databases. The search included articles published between 2000 and 2023, focusing on studies evaluating osteoporosis screening tools, Artificial intelligence applications in medical imaging, and implementing AI technologies in clinical settings.</div></div><div><h3>Study selection</h3><div>The Osteoporosis Risk Assessment Tool for Asians (OSTA), the Simple Calculated Osteoporosis Risk Estimator (SCORE), age, body size, one or no estrogen ever (ABONE), and the Osteoporosis Risk Index (OSIRIS) are the six commonly used screening tools for osteoporosis that are discussed in this review. In addition, the performance of AI-integrated imaging systems is explored in light of relevant research advances in Artificial intelligence in osteoporosis screening. Studies of the use of these tools in different populations and their advantages and disadvantages were included in the selection criteria.</div></div><div><h3>Results</h3><div>The results highlight that AI-integrated X-ray imaging technologies offer significant improvements over traditional osteoporosis screening tools. Artificial intelligence systems demonstrated higher accuracy by incorporating complex clinical data and providing personalized assessments for diverse populations. The studies showed that AI-driven imaging could enhance sensitivity and specificity, particularly in detecting early-stage bone density loss in patients with complex clinical profiles. The findings also suggest that Artificial intelligence technologies have the potential to be effectively applied in resource-limited settings through the use of mobile devices and remote diagnostics.</div></div><div><h3>Conclusions</h3><div>AI-integrated X-ray imaging technology significantly advances osteoporosis screening, offering more accurate and adaptable solutions than traditional tools. Its ability to incorporate complex clinical data and apply it across various demographic groups makes it particularly promising in diverse and resource-limited environments. Further research is needed to explore the full potential of AI in enhancing screening accessibility and effectiveness, particularly in underserved populations.</div></div>\",\"PeriodicalId\":10352,\"journal\":{\"name\":\"Clinical nutrition ESPEN\",\"volume\":\"64 \",\"pages\":\"Pages 207-214\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical nutrition ESPEN\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S240545772401338X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NUTRITION & DIETETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical nutrition ESPEN","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240545772401338X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
摘要
目的本综述旨在探讨现有骨质疏松症筛查工具的优势和困境,并提出可能的优化方法,同时探讨人工智能整合 X 射线成像在骨质疏松症筛查中的潜力,尤其是其提高准确性和适用于不同人群的能力。突破现有骨质疏松症筛查工具可及性低、临床转化率低、使用复杂、筛查结果局限性明显的困境。 数据来源 使用 PubMed、Web of Science 和 CNKI 数据库进行了全面的文献检索。研究选择亚洲人骨质疏松症风险评估工具(OSTA)、简单计算骨质疏松症风险估算器(SCORE)、年龄、体型、曾经使用过或未使用过雌激素(ABONE)以及骨质疏松症风险指数(OSIRIS)是本综述讨论的六种常用骨质疏松症筛查工具。此外,还根据人工智能在骨质疏松症筛查方面的相关研究进展,探讨了人工智能集成成像系统的性能。结果结果表明,与传统的骨质疏松症筛查工具相比,人工智能集成 X 光成像技术具有显著的改进。人工智能系统通过整合复杂的临床数据,为不同人群提供个性化评估,表现出更高的准确性。研究表明,人工智能驱动的成像技术可以提高灵敏度和特异性,尤其是在检测临床特征复杂的患者的早期骨密度损失方面。研究结果还表明,通过使用移动设备和远程诊断,人工智能技术有可能在资源有限的环境中得到有效应用。它能够整合复杂的临床数据,并将其应用于不同的人口群体,因此在多样化和资源有限的环境中特别有前景。要充分挖掘人工智能在提高筛查的可及性和有效性方面的潜力,尤其是在服务不足的人群中,还需要进一步的研究。
Current status and dilemmas of osteoporosis screening tools: A narrative review
Objective
This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, in addition to exploring the potential of AI-integrated X-ray imaging in osteoporosis screening, especially its ability to improve accuracy and applicability to different populations. To break through the dilemma of low accessibility, poor clinical translation, complexity of use, and apparent limitations of screening results of existing osteoporosis screening tools.
Data sources
A comprehensive literature search was performed using PubMed, Web of Science, and CNKI databases. The search included articles published between 2000 and 2023, focusing on studies evaluating osteoporosis screening tools, Artificial intelligence applications in medical imaging, and implementing AI technologies in clinical settings.
Study selection
The Osteoporosis Risk Assessment Tool for Asians (OSTA), the Simple Calculated Osteoporosis Risk Estimator (SCORE), age, body size, one or no estrogen ever (ABONE), and the Osteoporosis Risk Index (OSIRIS) are the six commonly used screening tools for osteoporosis that are discussed in this review. In addition, the performance of AI-integrated imaging systems is explored in light of relevant research advances in Artificial intelligence in osteoporosis screening. Studies of the use of these tools in different populations and their advantages and disadvantages were included in the selection criteria.
Results
The results highlight that AI-integrated X-ray imaging technologies offer significant improvements over traditional osteoporosis screening tools. Artificial intelligence systems demonstrated higher accuracy by incorporating complex clinical data and providing personalized assessments for diverse populations. The studies showed that AI-driven imaging could enhance sensitivity and specificity, particularly in detecting early-stage bone density loss in patients with complex clinical profiles. The findings also suggest that Artificial intelligence technologies have the potential to be effectively applied in resource-limited settings through the use of mobile devices and remote diagnostics.
Conclusions
AI-integrated X-ray imaging technology significantly advances osteoporosis screening, offering more accurate and adaptable solutions than traditional tools. Its ability to incorporate complex clinical data and apply it across various demographic groups makes it particularly promising in diverse and resource-limited environments. Further research is needed to explore the full potential of AI in enhancing screening accessibility and effectiveness, particularly in underserved populations.
期刊介绍:
Clinical Nutrition ESPEN is an electronic-only journal and is an official publication of the European Society for Clinical Nutrition and Metabolism (ESPEN). Nutrition and nutritional care have gained wide clinical and scientific interest during the past decades. The increasing knowledge of metabolic disturbances and nutritional assessment in chronic and acute diseases has stimulated rapid advances in design, development and clinical application of nutritional support. The aims of ESPEN are to encourage the rapid diffusion of knowledge and its application in the field of clinical nutrition and metabolism. Published bimonthly, Clinical Nutrition ESPEN focuses on publishing articles on the relationship between nutrition and disease in the setting of basic science and clinical practice. Clinical Nutrition ESPEN is available to all members of ESPEN and to all subscribers of Clinical Nutrition.