通过人工智能驱动的早期识别儿童耐火性便秘
Yi-Hsuan Huang1, Ruixuan Wan2, Yan Yang3
1Department of Gastroenterology, Children's Hospital of Nanjing Medical University, Nanjing, China.
机器学习可以使用结肠大小测量准确地识别患有耐火性便秘的儿童. 通过囊和ML模型的早期检测可以改善管理和生活质量.
科学领域:
- 儿科胃肠病学 儿科胃肠病学
- 医学成像分析 医学成像分析
- 医疗保健中的机器学习
背景情况:
- 儿童的抗性便秘导致严重的症状和降低生活质量,持续到成年.
- 早期发现耐火性便秘对于有效管理至关重要.
- 了解患有便秘类型的儿童的结肠解剖学差异至关重要.
研究的目的:
- 描述功能性便秘,耐火性便秘和没有便秘的儿童的结肠解剖学.
- 开发一种监督机器学习模型,用于早期识别儿童耐火性便秘.
主要方法:
- 追溯性研究分析了患者特征和标准化结肠大小 (SCS) 的比率,从三组的水溶液 (BE):功能性便秘 (n=77),耐火性便秘 (n=63),和非便秘 (n=65).
- 统计分析以确定结肠尺寸的显著差异.
- 使用SCS比率进行分类的监督机器学习模型的开发.
主要成果:
- 在三组中观察到直肠直径,直肠直径,直肠直径,直肠直径,直肠直径和直肠直径的显著差异.
- 一个线性支向量机器模型,使用五个SCS比率 (三角结肠,下垂结肠,横向结肠,直肠和直腰结肠),在分类中实现了81%的准确性.
- 该模型准确性的95%置信区间为79.17%至83.19%.
结论:
- 一个受监督的机器学习策略在区分患有耐火性便秘的儿童方面显示出81%的准确性.
- 将囊成像与机器学习模型相结合,为指导儿科耐火性便秘的管理提供了实际实用性.
- 这种方法在改善儿科便秘病例的临床决策方面具有显著的潜力.
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