人工智能和机器学习用于石头管理
Adithya Balasubramanian1, Hriday Bhambhvani1, Justin Lee2
1Department of Urology, Weill Cornell Medical College, Starr Pavilion, 525 East 68th Street 9th Floor, New York, NY 10065, USA; Department of Urology, Columbia Irving Medical Center, 161 Ft. Washington Avenue, 11th Floor, New York, NY 10032, USA.
人工智能和机器学习 (ML) 正在彻底改变结石管理. 这些技术增强了诊断,预测了治疗结果,并个性化了尿病患者的护理.
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
- 人工智能的人工智能
背景情况:
- 石头疾病的管理正在迅速推进新技术.
- 人工智能 (AI) 和机器学习 (ML) 为尿病护理提供了巨大的潜力.
- 目前的应用侧重于诊断,治疗和预防策略.
研究的目的:
- 探索ML算法在改善尿病导向成像中的作用.
- 评估ML在预测各种石材处理结果方面的潜力.
- 突出ML在优化石材组成分析和异常检测方面的作用.
主要方法:
- 审查关于ML在尿病中的应用的当前文献.
- 在诊断成像中分析ML算法潜力.
- 评估ML预测治疗成功的自发性石头通道,尿路透镜,冲击波石,和皮肤穿透性nephrolithotomy.
主要成果:
- ML算法在提高尿病成像的准确性方面表现有前途.
- 使用ML的预测模型可以改善自发石头通道和手术干预的结果.
- ML可以优化石头组成的分析和检测尿路异常.
结论:
- 基于ML的创新已经准备好个性化结石治疗.
- 这些技术将大大提高石头疾病管理的效率和有效性.
- 整合ML代表了泌尿器科护理的新前沿.
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