基于放射性特征,对COVID-19患者CT肺部发现的自动分类
Mahbubunnabi Tamal1, Murad Althobaiti1, Maryam Alhashim2,3
1Department of Biomedical Engineering, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
Biomedical physics & engineering express
|November 12, 2024
概括
手动选择的放射性特征可以准确地区分COVID-19肺部病理,如地面玻璃透明度 (GGO) 和整合. 这种方法提高了个性化治疗和监测疾病进展的严重性评分.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- COVID-19肺部CT扫描显示了地面玻璃不透明 (GGO),整合和膜溢出.
- 总经营组织往往在合并之前,并表现出异质的外观.
- 目前的严重性评分方法忽略了肺组织的外观,只关注卷入的区域.
研究的目的:
- 在COVID-19肺部CT图像中识别异质性/放射性特征,以区分GGO,巩固和肺部溢液.
- 在COVID-19肺部成像分析中建立特征选择的基准.
- 为了提高COVID-19严重程度评分的准确性.
主要方法:
- 从44个特征中评估了四种特征选择方法.
- 这些方法包括手动选择,基因算法-K-近邻 (GA-KNN),基因算法-二元决策树 (GA-BDT) 和基因算法-人工神经网络 (GA-ANN).
- 一个人工神经网络 (ANN) 在独立的数据集上使用选定的功能进行了训练和验证.
主要成果:
- 通过手动选择9个放射性特征,获得了最高的精度 (85.7%) 和AUC (0.90).
- 自动方法 (GA-BDT,GA-KNN,GA-ANN) 的准确性较低 (78%,77.5%,76.8%).
- 手动选择的特征显示出更高的灵敏度和特异性.
结论:
- 九个手动选择的放射性特征使得准确的COVID-19严重程度得分.
- 这种方法有助于临床医生规划个性化治疗和监测治疗反应.
- 这些功能对于跟踪COVID-19在临床试验中的进展和疗效非常有价值.
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