多重风险因素联合预测模型用于预测早产视网膜病变的风险预测
Shaobin Chen1, Xinyu Zhao2, Zhenquan Wu2
1Faculty of Applied Sciences, Macao Polytechnic University, Gomes Street, Macao, China.
The EPMA journal
|June 6, 2024
概括
人工智能 (AI) 与临床数据相结合,可以预测婴儿早产视网膜病变 (ROP) 和需要治疗的ROP (TR-ROP). 这种方法有助于早期检测,减少不必要的查,改善视觉结果.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 新生儿科学 新生儿科学
背景情况:
- 早产视网膜病变 (ROP) 是早产婴儿童年失明的重要原因之一.
- 早期识别和治疗对于改善视觉预后和预防失明至关重要.
- 预测,预防和个性化医学 (PPPM/3PM) 框架强调早期干预策略.
研究的目的:
- 开发一个人工智能 (AI) 算法,与临床人口统计学集成,用于ROP风险预测.
- 创建一个特定的风险模型来识别需要治疗的早产视网膜病变 (TR-ROP) 的婴儿.
主要方法:
- 利用了从22569名接受ROP查的婴儿的数据.
- 训练有素的后勤回归,决策树和多层感知子模型使用诸如出生体重 (BW),妊娠年龄 (GA),性别,多胎 (MB) 和分娩方式 (MD) 等因素.
- 使用AUC和AUCPR指标评估模型性能.
主要成果:
- 对于 ROP 预测,BW + GA 实现了最佳性能 (AUC: 0.8124 ± 0.0033).
- 对于TR-ROP预测,GA + BW + 性别 + MD + MB表现出合理的性能 (AUC: 0.8328 ± 0.0088).
结论:
- 人工智能与风险因素相结合,可以准确预测ROP和TR-ROP风险.
- 可以实现TR-ROP的早期检测,减少ROP检查和婴儿压力.
- 这种由人工智能驱动的方法为预测性诊断和个性化的ROP护理提供了具有成本效益的策略.
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