用SMOTE增强的可解释人工智能模型预测近视正常张力玻璃眼的视野进展
So Yeon Kim1,2, Jung Jong Jin3, Ahnul Ha4,5
1Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea.
Journal of glaucoma
|April 18, 2025
概括
使用合成少数群体过量采样技术 (SMOTE) 的AI模型准确地预测近视正常张力玻璃眼 (NTG) 患者的视野恶化. 疾病进展的关键预测因素是使用夏普利添加式扩张 (SHAP) 分析来确定的.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 近视正常张力玻璃眼 (NTG) 在预测视野进展方面存在挑战.
- 准确的预测对于及时干预和管理至关重要.
研究的目的:
- 开发和验证合成少数群体过量采样技术 (SMOTE) 增强的人工智能 (AI) 模型,用于预测近视NTG患者的视野进展.
- 通过SHAPley添加式扩展 (SHAP) 分析,识别疾病进展的关键预测因素.
主要方法:
- 来自近视NTG患者的100只眼睛的回顾性队列研究,平均随访时间为10.3年.
- 为了解决阶级不平衡,开发了一个SMOTE增强的AI模型.
- 用ROC分析,交叉验证和校准图表来评估模型性能.
- 使用SHAP分析来确定预测因素的重要性.
主要成果:
- 在28%的患者中观察到视野进展.
- 人工智能模型的AUC为0.83,灵敏度为0.81和特异性为0.77.
- 确定的主要预测因素包括基线平均偏差 (MD),年龄,轴长,基线眼内压力 (IOP) 和视野指数 (VFI).
- 更高的预测风险得分与观察到的进展率的增加有显著的相关性.
结论:
- 增强的SMOTE人工智能模型展示了近视NTG视野进展的有希望的预测性能.
- 该模型对个性化风险分层和早期干预具有潜在的临床实用性.
- 建议在更大的队列中进一步验证.
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