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：

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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。