在痴呆症中使用人工智能方法的临床预测模型.
Nicola Veronese1,2,3, Francesco Bolzetta4, Livia Gallo4
1Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, Via del Vespro, 141, 90127, Palermo, Italy. nicola.veronese@unipa.it.
Aging clinical and experimental research
|July 27, 2025
概括
人工智能 (AI) 模型对早期痴呆症检测有希望,实现了良好的预测准确性. 需要进一步的研究,以解决外部验证和数据代表性的临床整合问题.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 几乎一半的痴呆病例是可以预防的,这凸显了早期发现和干预的必要性.
- 人工智能 (AI) 增强的临床预测模型利用机器学习 (ML) 来整合各种数据,以改善痴呆症诊断和预后.
- 本系统性审查评估了基于人工智能的痴呆症发展,性能和临床使用的预测模型.
研究的目的:
- 系统地审查和评估基于人工智能的痴呆症预测模型的开发,性能和临床适用性.
- 评估人工智能模型的准确性,偏见和通用性,以预测痴呆症发病.
主要方法:
- 在PubMed,Embase和Web of Science的系统文献搜索,直到2024年10月,寻找预测痴呆症发病的AI模型.
- 评估了21项包括研究 (超过100万参与者) 使用PROBAST的准确性,偏见和通用性.
- 数据提取遵循TRIPOD和CHARMS框架,专注于研究设计,人口统计,预测因素和绩效指标.
主要成果:
- 人工智能模型在各种数据集中显示出良好的预测准确性 (平均AUC0.845).
- 机器学习方法,如随机森林和支持矢量机器,超过了传统模型的性能.
- 内部验证是常见的,但外部验证是有限的;校准和通用性仍然是挑战.
结论:
- 基于人工智能的预测模型为早期痴呆症检测和个性化护理策略提供了巨大的潜力.
- 临床整合需要解决外部验证,数据代表性和模型可解释性.
- 未来的研究应该优先考虑强大的验证和伦理考虑,以实现最佳的痴呆症护理实施.
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