深度学习使用计算机断层扫描来识别急性小肠阻塞的高风险患者:开发和验证预测模型:一项回顾性队列研究
Seungmin Oh1, Jongbin Ryu1,2, Ho-Jung Shin3
1Department of Artificial Intelligence, Ajou University, Suwon, South Korea.
International journal of surgery (London, England)
|September 18, 2023
概括
一个新的AI分类器使用CT扫描准确预测高度小肠阻塞 (HGSBO). 这种深度学习工具有助于识别需要紧急手术的患者,提高急性小肠阻塞 (SBO) 的诊断准确度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 手术决策支持手术决策支持
背景情况:
- 急性小肠阻塞 (SBO) 是紧急腹部手术的常见原因.
- 使用CT扫描,区分高等级SBO (HGSBO) 和低等级SBO (LGSBO) 或性阴茎是具有挑战性的.
- 人工智能和CT成像越来越多地用于指导急性腹部疾病的手术治疗决策.
研究的目的:
- 开发和验证一种新的3D卷积神经网络 (CNN) 分类器,用于预测HGSBO.
- 为了提高急性SBO患者诊断HGSBO的准确性.
- 创建可靠的AI驱动工具,用于SBO管理中的选和决策支持.
主要方法:
- 使用CT图像开发了一个深度学习分类器,WideResNet具有双分支架构和深度保留池,使用CT图像.
- 这项研究包括578例病例 (250例正常病例,209例HGSBO,119例LGSBO),CT图像超过38000张.
- 经过五次交叉验证后,使用准确度,灵敏度,特异性和AUC来评估分类性能.
主要成果:
- 3D CNN分类器实现了72.6%的精度和0.90.9的AUC.
- 关键绩效指标包括灵敏度为72.6%,特异性为86.3%,正预测值为74.1%,负预测值为86.6%.
- 该模型在测试集上预测HGSBO方面表现令人满意.
结论:
- 基于人工智能的新型3D分类器显示了对HGSBO预测的有希望的性能.
- 这种深度学习工具可以作为高风险SBO患者的有价值的查和决策支持系统.
- 开发的分类器在预测HGSBO方面优于以前的机器学习模型.
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