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使用深度卷积神经网络模型诊断甲状腺疾病,应用于甲状腺扫描图像:一个多中心研究

Huayi Zhao¹, Chenxi Zheng¹, Huihui Zhang¹

¹Department of Nuclear Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chong Qing, China.

Frontiers in endocrinology

|August 28, 2023

概括

一个深度卷积神经网络 (DCNN) 模型使用单光子发射计算机断层扫描 (SPECT) 图像显著提高了甲状腺疾病的诊断准确性,超过了核医学医生. 这种人工智能工具可以提高像Graves病这样的疾病的诊断效率.

科学领域:

医疗成像中的人工智能
核医学诊断核医学诊断
甲状腺疾病分类的分类.

背景情况:

准确诊断甲状腺疾病对于有效的患者管理至关重要.
核医学医生解释单光子发射计算机断层扫描 (SPECT) 图像用于甲状腺评估.
提高甲状腺疾病分类的诊断性能仍然是积极研究的领域.

研究的目的:

提高核医学医师在识别甲状腺疾病方面的诊断能力.
使用SPECT成像数据开发和验证一个深度卷积神经网络 (DCNN) 模型.
将DCNN模型的诊断性能与人类专家进行比较.

主要方法:

一个多中心的回顾性研究,涉及3194个SPECT甲状腺图像用于培训和验证.
四个预训练的DCNN模型 (AlexNet,ShuffleNetV2,MobileNetV3,ResNet-34) 被评估用于分类甲状腺疾病.
表现最好的模型 (ResNet-34) 经过交叉验证,并与初级和高级核医学医生进行了比较.

主要成果:

所有四种DCNN模型在分类SPECT甲状腺图像时都达到了超过0.85的准确性.
ResNet-34模型表现出卓越的性能,精度为0.944.
在内部 (94.4% vs. 85.6%) 和外部 (93.1% vs. 86.8%) 验证数据集中,ResNet-34模型显著优于高级医生.

结论:

深度卷积神经网络模型在从SPECT图像中诊断甲状腺疾病方面表现出很高的性能.
与核医学医生相比,DCNN模型对各种甲状腺疾病表现出更高的灵敏度和特异性.
深度学习模型具有显著的潜力,可以提高临床医生的核医学诊断效率.

关键词:

人工智能的人工智能是人工智能.深度卷积神经网络是一个深度卷积神经网络.核医学医生核医学医生甲状腺疾病是一种甲状腺疾病.甲状腺光学扫描是甲状腺光学扫描.

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