慢性鼻炎评估的深度学习衍生定量评分:与生活质量结果的相关性
Zhefan Shen1, Ying Wei2, Kexin Liu3
1Department of Radiology, Affiliated Hospital of Jiaxing University, Jiaxing, P. R. China.
American journal of rhinology & allergy
|January 17, 2025
概括
深度学习可以在CT扫描上实现自动化鼻细分,为慢性鼻炎 (CRS) 评估创造客观的定量得分. 这些新的得分显示出作为评估CRS严重程度的临床工具的前景.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 计算机断层扫描 (CT) 对于慢性鼻炎 (CRS) 的评估至关重要,但缺乏客观指标.
- 目前对CRS严重程度的评估方法是主观的,可能无法完全捕捉疾病负担.
研究的目的:
- 开发一种深度学习模型,用于CT扫描上的自动化鼻细分.
- 为了生成阴道变暗的定量得分,并评估它们与临床结果和生活质量的相关性.
主要方法:
- 一个深度学习模型 (nnU-Net) 在445个CT扫描上进行了训练,用于自动化鼻细分.
- 定量Lund-MacKay和不透明度得分是从细分结果中得出的.
- 分析了与隆德-麦凯分数 (Lund-MacKay score,LMS) 和鼻腔结局测试 (SNOT-22) 的相关性.
主要成果:
- 深度学习模型在训练,验证和测试集中实现了高细分精度 (迪斯系数高达0.993).
- 定量得分与LMS有很强的相关性 (rho=0.87,0.70; P<.001).
- 修改后的定量模糊度得分 (QOS) 与SNOT-22鼻腔和睡眠子域 (rho=0.26,0.27;P<.05) 显示了适度的相关性.
结论:
- 深度学习有助于自动化,从CT扫描中客观量化鼻模糊.
- 这些定量得分为CRS评估提供了一个有前途的工具,补充了现有的方法.
- 需要进一步的研究来验证它们在预测治疗反应和长期结果方面的有用性.
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