通过计算机断层扫描自动检测死亡软组织感染特征
Heng-Yu Lin1, Ming-Chuan Chiu2, Tzu-Lun Kao2
1Division of Plastic Surgery, Department of Surgery, Chi Mei Medical Center, Tainan 710, Taiwan.
Diagnostics (Basel, Switzerland)
|August 28, 2025
概括
使用YOLOv10的自动化系统有效地检测CT扫描中的软组织感染 (NSTI) 特征. 这种人工智能工具有助于更快地诊断和改善NSTI的手术规划.
科学领域:
- 医学成像
- 人工智能
- 放射学
背景情况:
- 死亡软组织感染 (NSTI) 需要及时诊断才能有效治疗.
- 计算机断层扫描 (CT) 对于识别NSTI特征至关重要.
- 自动检测系统可以提高诊断效率.
研究的目的:
- 开发和评估CT图像上的NSTI特征的自动检测系统.
- 在此任务中使用"你只看一次"版本10 (YOLOv10) 模型.
- 提高NSTI的诊断效率和手术规划.
主要方法:
- 31名经过手术确认的NSTIs患者的回顾性研究 (2017-2023年).
- 对于四个NSTI特征的9001CT图像的注释:子宫外气体,液体积累,膜胀和非增强.
- 使用平均精度 (mAP),回忆和精度进行性能评估.
主要成果:
- 在YOLOv10模型中,整体mAP为0.75.
- 回忆和精度分别为0.74和0.72.
- 粘膜 edematous 变化 (0. 92) 和软组织外宫气体 (0. 76) 的高回忆率.
结论:
- 基于YOLOv10的系统在CT检测关键NSTI特征方面表现出有效性.
- 这种人工智能方法有望改善NSTI诊断和手术规划.
- 该系统准确地识别软组织外气体,液体积累,带变化和软组织不增强.
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