预先训练有素的人工智能模型在动脉样硬化心血管疾病的预测和分类
Furkan Şakiroğlu1, Cemil Çolak2, Mehmet Cengiz Çolak3
1Biostatistics and Medical Informatics, Atatürk University Faculty of Medicine, Erzurum, Türkiye.
The Eurasian journal of medicine
|February 11, 2026
概括
人工智能 (AI) 显示出预测和管理动脉样硬化心血管疾病 (ASCVD) 的前景. 虽然人工智能比传统方法有优势,但数据偏差和可解释性等挑战需要进一步的研究和验证,以便在临床中使用.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 动脉样性心血管疾病 (ASCVD) 仍然是全球主要的死亡原因.
- 传统的ASCVD风险评估方法具有局限性.
- 人工智能 (AI) 和机器学习在风险评估方面越来越受青.
研究的目的:
- 系统地审查用于ASCVD预测和管理的AI技术.
- 分析应用于电子健康记录,医学成像和生物标志物的AI模型.
- 讨论AI在ASCVD中的潜力和局限性.
主要方法:
- 在ASCVD中对AI应用进行系统审查.
- 分析自然语言处理 (NLP) 模型 (例如,BERT) 来从临床文本预测风险.
- 评估卷积神经网络 (CNN) (例如ResNet,VGG) 用于通过成像进行斑块分析.
主要成果:
- 人工智能模型展示了从各种数据源预测ASCVD风险的潜力.
- NLP模型对分析文本临床数据充满希望.
- 在使用医学成像进行基于斑块的分析时,CNN是有效的.
结论:
- 人工智能为ASCVD管理提供了一个新的范式,改善了患者的治疗结果.
- 关键的挑战包括数据偏差,模型解释性和计算需求.
- 多中心验证和可解释的AI对于临床实施至关重要.
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