骨质疏松症放射诊断中的深度学习:一篇文献综述
The Journal of international medical research
|April 24, 2024
概括
深度学习显示了使用医学成像检查骨质疏松症查的前景. 然而,将这些人工智能诊断模型引入临床实践仍然存在挑战.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 骨质疏松症是一种重要的骨疾病,导致骨折.
- 人工智能 (AI) 越来越多地应用于医学诊断.
- 深度学习 (DL) 显示出从放射图像中识别骨质疏松症的潜力.
研究的目的:
- 审查深度学习方法在骨质疏松症放射诊断中的应用.
- 总结目前对DL用于骨质疏松症查和诊断的研究.
主要方法:
- 在PubMed和Web of Science数据库中的系统文献搜索.
- 专注于使用常规放射学方法 (X射线,CT,MRI) 进行机会性骨质疏松症查的研究.
- 在最终审查中包括了40项研究.
主要成果:
- 研究被分为骨质疏松症查 (20),骨矿物密度预测 (13),骨折风险预测/检测 (7).
- 深度学习模型展示了从医学图像中查骨质疏松症的显著能力.
- 人工智能驱动的方法显示出预测骨矿物质密度和骨折风险的潜力.
结论:
- 深度学习对骨质疏松症查应用有很大的潜力.
- 基于DL的骨质疏松症诊断模型的临床实施和商业化面临着挑战.
- 需要进一步的研究来弥合人工智能开发和临床实践之间的差距.
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