基于超声波图像的诺姆图结合了临床,放射学和深度转移学习特征,用于根据O-RADS对卵巢质量的自动分类
Lu Liu1, Wenjun Cai2, Hongyan Tian1
1Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Frontiers in oncology
|May 30, 2024
概括
结合临床,放射学和深度学习特征的新型超声波名图准确地分类了卵巢质量. 这种AI工具有助于初级放射科医生诊断恶性瘤风险,改善卵巢癌查.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 瘤学和诊断工具.
- 放射学和机器学习应用程序.
背景情况:
- 准确区分良性和恶性卵巢质量对于患者管理至关重要.
- 目前的诊断方法需要改进,以便快速准确地进行分类.
- 卵巢-尾报告和数据系统 (O-RADS) 提供了一个标准化的分类框架.
研究的目的:
- 开发一种基于超声波图像的自动化名录,用于分类卵巢质量.
- 整合临床,放射学和深度转移学习功能,以提高诊断准确度.
- 根据O-RADS标准将群体分为低风险和中高风险的恶性瘤.
主要方法:
- 包括1,080张来自卵巢质量患者的超声波图像.
- 使用来自不同医院的培训队列 (683名患者) 和测试队列 (397名患者).
- 采用了一种工作流程,涉及图像分割,特征提取,特征选择和模型构建,使用预训练的Resnet-101模型.
主要成果:
- 开发的诺米图表显示出高的诊断性能 (AUC:0.930,准确率:84.9%).
- 诺米图表表现优于初级放射科医生,并且在作为辅助工具时显著提高了他们的诊断能力.
- 校准曲线证实了良好的协议,决策曲线分析表明了临床效用.
结论:
- 人工智能驱动的诺米图表显示了对卵巢质量的令人满意的诊断性能.
- 这种工具有可能提高初级放射科医生的专业知识.
- 它提供了一种快速有效的方法来改善卵巢癌查和风险分层.
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Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...
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