用3D CNN自动检测脑部CT中的出血，提升急诊诊断速度和准确率。
3D Convolutional Neural Networks for Improved Detection of Intracranial bleeding in CT Imaging
- 采用U型3D卷积网络分析体积CT影像，保留空间上下文信息
- 对四种出血类型检测精度超90%，硬膜外出血精确率达96%
- 适合急诊科快速筛查，助力临床决策提速
颅内出血（IB）是创伤性脑损伤导致的危重病症，包括硬膜外、硬膜下、蛛网膜下腔及脑实质出血。快速准确识别对预防严重并发症至关重要。传统影像评估耗时且易受主观影响，尤其在高压环境下。人工智能（AI）可通过快速分析医学影像，识别细微出血并标记紧急病例，显著提升诊断效率与准确性，优化临床流程。本文提出一种U型3D卷积神经网络（CNN），用于全自动检测与分类体积分层CT中的颅内出血。通过CLAHE增强与强度归一化等预处理技术提升图像质量。基于2,912例标注的CT扫描数据集进行训练与评估。结果显示，模型在主要出血类型上表现优异，多数情况的精确率、召回率与准确率均超过90%；其中硬膜外出血精确率达96%，蛛网膜下腔出血准确率为94%。该模型具备良好的定位与分类能力，展现出临床可靠性。结论表明，该3D CNN为自动化颅内出血检测提供可扩展方案，有助于减少诊断延迟，改善急诊救治结果。未来工作将扩充数据多样性，优化实时处理性能，并融合多模态数据以增强临床实用性。
原文摘要 · Abstract (English)
Background: Intracranial bleeding (IB) is a life-threatening condition caused by traumatic brain injuries, including epidural, subdural, subarachnoid, and intraparenchymal hemorrhages. Rapid and accurate detection is crucial to prevent severe complications. Traditional imaging can be slow and prone to variability, especially in high-pressure scenarios. Artificial Intelligence (AI) provides a solution by quickly analyzing medical images, identifying subtle hemorrhages, and flagging urgent cases. By enhancing diagnostic speed and accuracy, AI improves workflows and patient care. This article explores AI's role in transforming IB detection in emergency settings. Methods: A U-shaped 3D Convolutional Neural Network (CNN) automates IB detection and classification in volumetric CT scans. Advanced preprocessing, including CLAHE and intensity normalization, enhances image quality. The architecture preserves spatial and contextual details for precise segmentation. A dataset of 2,912 annotated CT scans was used for training and evaluation. Results: The model achieved high performance across major bleed types, with precision, recall, and accuracy exceeding 90 percent in most cases 96 percent precision for epidural hemorrhages and 94 percent accuracy for subarachnoid hemorrhages. Its ability to classify and localize hemorrhages highlights its clinical reliability. Conclusion: This U-shaped 3D CNN offers a scalable solution for automating IB detection, reducing diagnostic delays, and improving emergency care outcomes. Future work will expand dataset diversity, optimize real-time processing, and integrate multimodal data for enhanced clinical applicability.
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