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Automatic detection of pituitary microadenoma from magnetic resonance imaging using deep learning algorithms
medRxiv - Radiology and Imaging Pub Date : 2021-03-05 , DOI: 10.1101/2021.03.02.21252010
Qingling Li , Yanhua Zhu , Minglin Chen , Ruomi Guo , Qingyong Hu , Zhenghui Deng , Songqing Deng , Huiquan Wen , Rong Gao , Yuanpeng Nie , Haicheng Li , Tiecheng Zhang , Jianning Chen , Guojun Shi , Jun Shen , Wai Wilson Cheung , Yulan Guo , Yanming Chen

Pituitary microadenoma (PM) is often difficult to detect by MR imaging alone. We employed a computer-aided PM diagnosis (PM-CAD) system based on deep learning to assist radiologists in clinical workflow. We enrolled 1,228 participants and stratified into 3 non-overlapping cohorts for training, validation and testing purposes. Our PM-CAD system outperformed 6 existing established convolutional neural network models for detection of PM. In test dataset, diagnostic accuracy of PM-CAD system was comparable to radiologists with > 10 years of professional expertise (94% versus 95%). The diagnostic accuracy in internal and external dataset was 94% and 90%, respectively. Importantly, PM-CAD system detected the presence of PM that had been previously misdiagnosed by radiologists. This is the first report showing that PM-CAD system is a viable tool for detecting PM. Our results suggest that PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

中文翻译:

使用深度学习算法从磁共振成像自动检测垂体微腺瘤

垂体微腺瘤(PM)通常很难仅通过MR成像来检测。我们采用了基于深度学习的计算机辅助PM诊断(PM-CAD)系统,以协助放射科医生进行临床工作流程。我们招募了1,228名参与者,并将其分为3个不重叠的队列,以进行培训,验证和测试。我们的PM-CAD系统优于6个已建立的用于检测PM的卷积神经网络模型。在测试数据集中,PM-CAD系统的诊断准确性可与具有10年以上专业知识的放射线医生相媲美(94%对95%)。内部和外部数据集的诊断准确性分别为94%和90%。重要的是,PM-CAD系统检测到先前被放射科医生误诊的PM。这是第一份表明PM-CAD系统是检测PM的可行工具的报告。我们的结果表明,PM-CAD系统适用于放射科,尤其是初级卫生保健机构。
更新日期:2021-03-05
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