通过人工智能识别的原发性自发性肺胸病的放射性胸壁异常
Ming-Chuan Chiu1, Stella Chin-Shaw Tsai2,3, Zhe-Rui Bai1
1Department of Industrial Engineering and Industrial Management, National Tsing Hua University, Hsinchu, 300044, Taiwan.
Heliyon
|May 10, 2024
概括
年轻男性的原发性自发性肺胸炎 (PSP) 可能与胸壁结构异常有关,例如胸壁顶穴 (CAP). 对CT扫描的人工智能分析准确地确定了CAP,表明其病因作用和改善诊断的潜力.
科学领域:
- 放射学 放射学是一门学科.
- 医学成像分析 医学成像分析
- 人工智能在医学中的应用
背景情况:
- 初级自发性肺胸炎 (PSP) 主要影响年轻,高大,瘦身的男性.
- 了解胸壁结构和PSP之间的病因联系对于改善治疗策略至关重要.
- 胸壁的结构特征尚未被广泛探索为PSP的直接病因因素.
研究的目的:
- 调查胸壁特定结构特征与初级自发性肺胸部 (PSP) 之间的关联.
- 利用人工智能 (AI),特别是卷积神经网络 (CNN),用于分析胸部计算机断层扫描 (CT) 图像以识别与PSP相关的特征.
- 确定胸壁顶峰坑 (CAP) 是否是PSP的潜在病因因素.
主要方法:
- 一项病例控制研究涉及26名PSP患者和26名非PSP患者的胸部CT图像.
- 应用人工智能模型 (EfficientNetB3,InceptionV3) 与转移学习用于图像分析.
- 使用热图和规模不变特征转换 (SIFT) 来进行详细的特征比较和验证.
主要成果:
- 胸壁顶部坑 (CAP) 在PSP患者 (25/26) 与非PSP患者 (3/26) 相比显著更为普遍 (p < 0.001).
- 通过识别CAP,CNN模型在区分PSP与非PSP方面实现了93.47%的准确性.
- 人工智能分析,包括热图可视化,精确地准了上胸壁特征,证实了CAP的识别.
结论:
- 在PSP患者中观察到独特的放射性胸壁配置,特别是CAP.
- CAP可能是一个重要的病因因素,有助于PSP的发展.
- 人工智能辅助的CT扫描分析显示,改进PSP的诊断方法和治疗策略具有前景.
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