评估深度学习模型的低诊断和过度诊断偏差在服务不足的人群中对初级开角青光眼诊断的偏差
Mingquan Lin1, Yunyu Xiao1, Bojian Hou2
1Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
概括
对于初级开角眼 (POAG) 检测的深度学习模型显示偏差,低诊断年轻女性和过度诊断年长的黑人个体. 这引发了眼科中服务不足的人群的伦理问题.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 主要开角玻璃眼 (POAG) 是美国导致失明的主要原因,不成比例地影响非洲裔美国人和西班牙裔人.
- 在深层图像上训练的深度学习模型显示性能与POAG的临床诊断相当或超过.
- 临床诊断中的现有偏见可能会被深度学习模型放大,导致诊断不足或过度诊断.
研究的目的:
- 检查用于POAG检测的深度学习模型中的不足诊断和过度诊断.
- 调查这些偏见对服务不足的人群的影响,使用眼睛高血压治疗研究 (OHTS) 的数据.
主要方法:
- 分析深度学习模型在POAG检测上的表现,使用来自眼睛高血压治疗研究 (OHTS) 的 fundus 图像.
- 评估不同人口群体的诊断准确性,包括年龄和种族.
主要成果:
- 深度学习模型表现出偏差的表现,导致年轻女性的诊断不足,老年黑人人过度诊断.
- 这些诊断差异可能导致特定的人口群体的治疗延迟或不必要的干预.
结论:
- 广泛用于POAG检测的深度学习模型表现出不成比例地影响服务不足的人群的偏见.
- 偏见的眼科人工智能引发了有关公平医疗保健和患者结果的伦理担忧.
- 需要进一步的研究来缓解眼科诊断的AI算法中的偏差.
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