肺炎-Plus:基于CT断层扫描的细菌,真菌和病毒性肺炎的分类深度学习模型
Fang Wang1, Xiaoming Li1, Ru Wen2
1Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), 30 Gao Tan Yan St, Chongqing, 400038, China.
European radiology
|June 30, 2023
概括
一个新的深度学习算法,肺炎-Plus,使用CT扫描准确地分类细菌,真菌和病毒性肺炎. 这种人工智能工具显示出有助于临床决策和减少误诊的潜力,达到经验丰富的放射科医生的性能水平.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 精确的肺炎分类对于有效治疗至关重要.
- 区分细菌性,真菌性和病毒性肺炎可能是一个挑战.
- 深度学习有可能提高医学成像诊断的准确性.
研究的目的:
- 开发和验证Pneumonia-Plus深度学习算法,用于分类肺炎类型.
- 评估算法的性能与专家放射科医生对比.
主要方法:
- 在2763张胸部CT图像上训练了一种深度学习算法 (肺炎-Plus).
- 在173名患者中,前性验证了算法.
- 将算法性能与使用McNemar测试的三个放射科医生进行了比较.
主要成果:
- 肺炎-Plus获得的AUC为0.816 (病毒),0.715 (真菌) 和0.934 (细菌).
- 该算法在病毒性肺炎 (0.847,0.919,0.873) 中表现出高灵敏度,特异性和准确性.
- 在细菌和病毒性肺炎的分类中,肺炎-Plus的表现优于两名放射科医生 (p < 0.05).
结论:
- 肺炎-Plus算法准确地区分了细菌,真菌和病毒性肺炎.
- 该算法的性能与正在治疗的放射科医生相当.
- 这种人工智能工具可以指导临床决策,减少过度使用抗生素,改善患者的治疗结果.
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