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深度神经网络在甲状腺瘤病理分类中的价值

Chengwen Deng¹, Dan Li², Ming Feng³

¹Department of Nuclear Medicine, Chaohu Hospital of Anhui Medical University, Heifei, 238000, China.

Diagnostic pathology

|August 19, 2023

概括

深度神经网络 (DNN) 在从病理图像中分类甲状腺瘤方面表现有前途,达到高精度. 这些模型为有效的甲状腺状况组织病理学诊断提供了潜力.

科学领域:

计算病理学计算病理学
医学中的人工智能.
甲状腺癌的诊断甲状腺癌的诊断

背景情况:

深度神经网络 (DNN) 为分析复杂的病理图像提供了潜力.
准确诊断甲状腺瘤对于有效的治疗计划至关重要.

研究的目的:

为了评估甲状腺瘤病理学图像的DNNs的诊断价值.
探索DNN在甲状腺癌诊断中的临床应用潜力.

主要方法:

追溯分析了来自559名患者的799张甲状腺病理图像.
在四个DNN模型 (Resnet50,Resnext50,EfficientNet,Densenet121) 的培训和测试中.
包括各种甲状腺病理:乳头,髓,卵泡甲状腺癌,腺,腺瘤和正常甲状腺.

主要成果:

DNN模型显示出高诊断效率,AUC高达0.994和卡帕系数高达0.7713.
记住,精度,NPV,准确性,特异性和F1分数在不同模型和病理学上有所不同.
在诊断卵泡性甲状腺癌,腺瘤和腺瘤囊时,观察到略低的性能.

结论:

DNN模型在基于组织病理学图像的基础上对甲状腺瘤进行分类,取得了令人满意的结果.
这项研究强调了DNN在有效诊断甲状腺瘤组织病理学方面的潜力.
进一步的研究可能会对挑战性甲状腺病理的DNN性能进行改进.

关键词:

人工智能的人工智能是人工智能.深度神经网络是一个神经网络.诊断诊断诊断诊断病理学病理学病理学甲状腺瘤的发生在甲状腺上.

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