神经网络应用程序用于评估甲状腺相关的轨道病变活动,使用轨道计算机断层扫描
Jaesung Lee1,2, Sanghyuck Lee1, Won Jun Lee3
1Department of Artificial Intelligence, Chung-Ang University, Seoul, Korea.
Scientific reports
|August 10, 2023
概括
本研究介绍了一种神经网络 (NN) 方法,用于评估使用轨道CT扫描的甲状腺相关轨道病变 (TAO) 活动. 作为一种诊断工具,NN显示出前景,在准确性方面超过其他模型.
科学领域:
- 眼科医生 眼科 眼科
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 甲状腺相关轨道病 (TAO) 是一种影响眼的自身免疫性疾病.
- 准确评估TAO活动对于及时有效治疗至关重要.
- 当前的诊断方法在区分活跃和不活跃的疾病阶段时可能缺乏精度.
研究的目的:
- 开发和验证一种基于神经网络 (NN) 的新方法来评估TAO患者的活动.
- 使用轨道计算机断层扫描 (CT) 扫描和临床数据用于TAO活动评估.
- 将拟议的NN的性能与现有的深度学习模型进行比较.
主要方法:
- 分析了144名活跃TAO患者和288名不活跃TAO患者的轨道CT扫描.
- 一个预处理管道涉及选择11个关键图像切片和细分感兴趣的区域.
- 设计了一个定制的NN,整合了来自13个信息提取管道的数据和患者年龄/性别数据.
主要成果:
- 拟议的NN实现了0.871的接收器操作曲线 (AUROC) 下的面积,灵敏度为0.786和特异性为0.779.
- 性能明显超过了对比模型CSPDenseNet (AUROC 0.819) 和ConvNeXt (AUROC 0.774) 的性能.
- 废弃性研究表明,在三到五个活跃信息管道的情况下,NN的性能是最佳的.
结论:
- 开发的基于NN的方法显示出作为诊断TAO活动的可靠工具的高潜力.
- 这些发现表明,整合多管道信息提取可以提高诊断准确度.
- 建议进一步验证,以确定这种方法作为TAO评估的临床标准.
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