在骨质疏松症管理中开发和报告人工智能
Guillaume Gatineau1, Enisa Shevroja1, Colin Vendrami1
1Interdisciplinary Center of Bone Diseases, Rheumatology Unit, Bone and Joint Department, Lausanne University Hospital and University of Lausanne, Av. Pierre-Decker 4, 1011 Lausanne, Switzerland.
概括
人工智能 (AI) 在骨质疏松症研究中表现有前途,有助于诊断和决策. 然而,不一致的报告和质量差异需要标准化的实践,以便可靠地将AI整合到骨健康中.
科学领域:
- 骨科和骨健康 骨科和骨健康
- 医学的人工智能 (AI)
- 放射学和成像分析分析.
背景情况:
- 医学数据和计算能力的指数增长推动了骨和骨质疏松症研究中的AI应用.
- 越来越多的人工智能研究需要透明的模型开发和报告策略,以便可靠的临床整合.
研究的目的:
- 在骨质疏松症研究中对AI文章进行全面的审查和系统的质量评估.
- 突出最近的进展,并确定骨健康人工智能应用的趋势.
主要方法:
- 在PubMed数据库 (2020年12月 - 2023年2月) 系统搜索人工智能和骨质疏松相关的文章.
- 通过使用临床人工智能建模 (MI-CLAIM) 检查清单中最低信息的12项,对97项已识别的研究进行质量评估.
- 研究对骨属性评估,骨质疏松症分类,骨折检测,风险预测和骨细分的分类.
主要成果:
- 在五个关键领域确定了97篇文章:骨质特性 (11),分类 (26),骨折检测 (25),风险预测 (24) 和细分 (11).
- 平均质量得分有所不同,骨段 (9.0/11) 和骨属性评估 (8.9/11) 得分最高,而风险预测 (7.6/11) 和骨质疏松症分类 (7.8/11) 得分较低.
- 人工智能驱动的临床决策支持成为第六个领域,专注于提高临床医生的效率和患者的结果.
结论:
- 观察到研究质量的显著差异和缺乏标准化的报告实践.
- 尽管存在局限性,但人工智能模型显示出早期骨质疏松症诊断和改善临床决策的潜力.
- 解决人工智能模型评估中的偏见对于建立信心和增强骨健康的临床工作流程至关重要.
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