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人工智能在骨折风险预测中的潜在作用

Namki Hong¹, Danielle E Whittier², Claus-C Glüer³

¹Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; Institute for Innovation in Digital Healthcare, Yonsei University Health System, Seoul, Korea.

The lancet. Diabetes & endocrinology

|June 28, 2024

概括

人工智能和机器学习可以通过识别骨折高风险个体来改善骨质疏松症的管理. 这些技术提供了个性化的治疗策略,以减少骨质疏松性骨折的全球负担.

科学领域:

老年学是一门学科.
生物医学信息学生物医学信息学
整形外科整形外科整形外科

背景情况:

骨质疏松性骨折对健康造成重大负担,特别是在老年人中.
目前的骨质疏松症疗法在高风险人群中未得到充分利用,因此需要改进骨折风险评估和病例发现方法.

研究的目的:

探索人工智能 (AI) 和机器学习 (ML) 在提高骨质疏松症和骨折风险的识别和管理方面的潜力.
概述AI-ML如何改善机会性查,个性化监测和多式模式骨折预测.

主要方法:

利用AI-ML分析来自医疗记录,成像和可穿戴设备的高维数据.
整合多式联运特征,提高骨折预测和风险分层.
考虑AI-ML模型的可解释性,偏见和临床整合挑战.

主要成果:

人工智能ML可以自动化对脊椎骨折和骨质疏松症的机会性查.
人工智能ML可实现基于家庭的监测和生活方式干预的定位.
通过AI-ML进行多模式数据集成可以显著提高断裂预测的准确性.

结论:

人工智能-ML算法具有骨质疏松症管理的变革潜力,使个性化治疗方法成为可能.
解决AI-ML可解释性,偏见和工作负载影响对于成功的临床整合至关重要.
实施AI-ML可以带来更积极和个性化的战略,以减少骨质疏松性骨折的发生率.

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Fractures: Bone Repair

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Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
Minor fractures with no bone displacement are treated by immobilizing the fractured bone using a cast or splint. However, in the case of fractures with displaced bones, the broken bones are repositioned before immobilization to ensure successful healing without deformation and loss of function. The realignment of fractured bone ends is performed through a process called reduction. If the...