用人工神经网络模型预测重复冲击波石治疗上尿路结石患者的有效性
Zhongfan Peng1, Mingjun Wen2, Yunfei Li3
1Hubei University of Medicine of Nursing, Shiyan, China. 20120506@hbmu.edu.cn.
Urology journal
|April 17, 2024
概括
这项研究开发了一种人工神经网络 (ANN) 模型,用于预测反复冲击波石 (SWL) 对上泌尿道结石的成功. 该模型准确地识别了影响SWL疗效的因素,有助于治疗决策和减少患者负担.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 冲击波石 (SWL) 是一种常见的治疗上泌尿道结石 (UUTC).
- 很大一部分患者在最初的失败尝试后需要重复SWL.
- 预测重复SWL的疗效对于优化治疗策略和患者的治疗结果至关重要.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 模型,用于预测UUTC患者重复SWL的成功.
- 确定影响重复SWL疗效的关键因素.
- 帮助选择有失败SWL病史的患者合适的治疗计划,从而减少治疗负担.
主要方法:
- 对304名接受UUTC重复SWL的患者的临床和成像数据的回顾性分析.
- 使用14个输入变量开发ANN模型,包括石材特征 (平均石材密度,石材体积,石材异质指数) 和患者因素 (年龄,BMI,吸烟,酒精使用).
- 使用接收器运行特征 (ROC) 曲线验证模型,并计算曲线下的面积 (AUC) 和预测准确度.
主要成果:
- 该研究包括304名患者,重复SWL的成功率为50.7%.
- 在验证组中,ANN模型实现了0.852的AUC和83.3%的预测准确度.
- 重复SWL成功的最重要的预测因素是平均石材密度 (MSD),石材体积 (SV) 和皮肤到石材距离 (SSD).
结论:
- 一个ANN模型可以有效地预测UUTC重复SWL的成功.
- 这种预测模型对于优化患者护理和在初始SWL失败后定制治疗计划非常有价值.
- 实施这种模型可以提高重复SWL的成功率,并降低患者的整体治疗负担.
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