缓解人工智能驱动的心血管成像中的算法偏差,以获得更公平的诊断
Md Abu Sufian1,2, Lujain Alsadder3, Wahiba Hamzi4
1IVR Low-Carbon Research Institute, Chang'an University, Xi'an 710018, China.
Diagnostics (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了公平意识的算法和敏感载体感染恢复 (SCIR) 模型,以减少心血管深度学习预测中的偏差. 这项研究成功地缓解了健康差异,增强了AI对临床应用的信任.
科学领域:
- 人工智能的人工智能
- 心血管医学 心血管医学
- 医疗成像医学成像
背景情况:
- 对于心血管风险预测的深度学习模型中的算法偏差加剧了健康差异.
- 现有的模式缺乏跨人口和社会经济群体的公平性,需要采取缓解偏见的策略.
- 公平意识的算法和可解释的AI的整合对于公平的医疗保健至关重要.
研究的目的:
- 解决心血管风险预测深度学习中的算法偏差.
- 整合公平意识的算法和敏感载体感染恢复 (SCIR) 模型,以获得公平的AI洞察力.
- 提高诊断精度,减少弱势群体之间的差异.
主要方法:
- 利用心脏3D/4D磁共振成像和心脏图谱项目 (CAP) 的表格数据.
- 调整了SCIR模型,使用对抗式调试,Fairlearn和均等赔率后处理.
- 实现了YOLOv5,Mask R-CNN和ResNet18与LIME和SHAP一起进行解释.
主要成果:
- 偏差缓解改善了差异影响 (0.80至0.95) 并减少了机会平等差异 (0.20至0.05).
- SCIR模型表现出高交叉与联盟 (IoU) 分数 (94.8%) 和子系数 (0.941-0.980) 的稳定性.
- 可解释性模型 (YOLOv5,Mask R-CNN,ResNet) 在细分和分类方面表现强.
结论:
- 公平意识的算法有效地解决了心血管预测模型中的偏见.
- 公平性和可解释的AI的整合促进了公平的诊断精度.
- 该研究减少了诊断差异,增强了对AI系统的临床信任.
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