卷积神经网络算法的诊断准确性,以区分胃肠道阻塞在儿童群体的常规放射图上
Ercan Ayaz1, Hasan Güçlü2, Ayşe Betül Oktay3
1Diyarbakır Children's Hospital, Radiology Clinic, Diyarbakır; Current: University of Health Sciences Türkiye, Başakşehir Çam and Sakura City Hospital, Department of Radiology, İstanbul, Türkiye.
Diagnostic and interventional radiology (Ankara, Turkey)
|February 28, 2025
概括
深度学习模型可以通过X射线图准确地检测儿科胃肠道阻塞. 这些模型可以区分手术和非手术病例,帮助急诊室的决策.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科成像学 儿科成像学
背景情况:
- 胃肠道 (GI) 扩张在儿科急诊室访问中很常见.
- 准确区分手术阻塞对于预防严重并发症至关重要.
研究的目的:
- 开发卷积神经网络 (CNN) 模型,以区分正常的儿科胃肠道气体分布与扩张或阻塞.
- 区分需要手术治疗的儿科胃肠道阻塞和非手术治疗的胃肠道阻塞.
主要方法:
- 在腹部X射线图上使用转移学习训练了五个CNN模型 (ResNet50,InceptionResNetV2,Xception,EfficientNetV2L,ConvNeXtXLarge).
- 使用了正常,手术纠正扩张 (SD) 和炎症/感染扩张 (ID) 病例的数据集.
- 评估模型性能,有或没有自动化作物预处理.
主要成果:
- 美国有线电视新闻网的模型在区分正常和异常的肠道图像方面取得了很高的准确性 (高达96.9%的剪切).
- 在ResNet50和InceptionResNetV2中,初始准确度高 (93.3%和90.6%).
- EfficientNetV2L在区分手术纠正和炎症/传染性扩张方面实现了94.6%的准确性.
结论:
- 深度学习模型在紧急情况下为儿科胃肠道阻塞提供了高度准确的决策支持系统.
- 这项研究独特地将CNN应用于儿科患者群体,区分手术需求.
- 这些模型可以提醒医生注意异常的X射线图和潜在的病因,改善患者的护理.
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