用深度学习自动识别儿童阑尾炎超声图像，准确率超93%。
Pediatric Appendicitis Detection from Ultrasound Images
- 基于预训练ResNet模型，微调后区分阑尾炎与非阑尾炎图像。
- 在多视角超声数据上达到93.44%准确率，召回率达89.8%。
- 有效应对低对比度、噪声和解剖变异，适合临床辅助诊断。
儿童阑尾炎是儿科急性腹痛的常见原因，其诊断因症状重叠和影像质量差异而具挑战性。本研究基于预训练的ResNet架构，开发并评估了一种自动化检测儿童超声图像中阑尾炎的深度学习模型。采用德国雷根斯堡儿童医院提供的雷根斯堡儿科阑尾炎数据集，包含1至15个右下腹区域超声视图，涵盖阑尾、淋巴结及相关结构。图像经归一化、缩放和增强处理以提升泛化能力。微调后的ResNet模型在图像分类任务中实现93.44%准确率、91.53%精确率和89.8%召回率，表现出色。模型成功学习到具有判别性的空间特征，克服了儿童超声图像中低对比度、斑点噪声和解剖变异带来的挑战。
原文摘要 · Abstract (English)
Pediatric appendicitis remains one of the most common causes of acute abdominal pain in children, and its diagnosis continues to challenge clinicians due to overlapping symptoms and variable imaging quality. This study aims to develop and evaluate a deep learning model based on a pretrained ResNet architecture for automated detection of appendicitis from ultrasound images. We used the Regensburg Pediatric Appendicitis Dataset, which includes ultrasound scans, laboratory data, and clinical scores from pediatric patients admitted with abdominal pain to Children Hospital. Hedwig in Regensburg, Germany. Each subject had 1 to 15 ultrasound views covering the right lower quadrant, appendix, lymph nodes, and related structures. For the image based classification task, ResNet was fine tuned to distinguish appendicitis from non-appendicitis cases. Images were preprocessed by normalization, resizing, and augmentation to enhance generalization. The proposed ResNet model achieved an overall accuracy of 93.44, precision of 91.53, and recall of 89.8, demonstrating strong performance in identifying appendicitis across heterogeneous ultrasound views. The model effectively learned discriminative spatial features, overcoming challenges posed by low contrast, speckle noise, and anatomical variability in pediatric imaging.
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