PDF(Pubmed)
Abstract：
BACKGROUND: Breast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practice is problematic because of the costs of introducing such approaches to hospital information systems (HISs) and the security risk of connecting HIS to the Internet to access AI services. To solve these problems, we developed a system that applies AI to the analysis of breast US images captured using a smartphone.
METHODS: Training data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training.
RESULTS: The established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions.
CONCLUSIONS: Good-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.
METHODS: Training data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training.
RESULTS: The established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions.
CONCLUSIONS: Good-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.
摘要：
背景：乳房超声（US）对致密乳房很有用，应考虑引入人工智能（AI）辅助诊断乳腺US图像。然而,基于人工智能的技术在临床实践中的实施是有问题的，因为在医院信息系统（HIS）中引入此类方法的成本以及将HIS连接到互联网以访问人工智能服务的安全风险。为了解决这些问题,我们开发了一种系统，该系统将AI应用于分析使用智能手机捕获的美国乳房图像。
方法：使用乳腺外科获得的115张良性病变图像和201张恶性病变图像准备训练数据，岐阜大学医院。使用YOLOv3(对象检测模型)来检测US图像上的病变。开发了图形用户界面（GUI）来预测AI服务器。还开发了智能手机应用程序，用于使用其摄像头捕获显示在HIS监视器上的美国图像，并显示从AI服务器接收到的预测结果。在AI服务器上和通过智能手机执行的预测的灵敏度和特异性是使用从训练中省去的60张图像计算的。
结果：已建立的AI对恶性病变显示出100％的敏感性和75％的特异性，并且每次使用AI服务器进行预测需要0.2s。使用智能手机进行预测需要2s，并且对恶性病变显示出100％的敏感性和97.5％的特异性。
结论：使用AI服务器获得了良好的质量预测。此外，通过智能手机进行预测的质量略好于人工智能服务器，可以安全，廉价地引入HIS。
方法：使用乳腺外科获得的115张良性病变图像和201张恶性病变图像准备训练数据，岐阜大学医院。使用YOLOv3(对象检测模型)来检测US图像上的病变。开发了图形用户界面（GUI）来预测AI服务器。还开发了智能手机应用程序，用于使用其摄像头捕获显示在HIS监视器上的美国图像，并显示从AI服务器接收到的预测结果。在AI服务器上和通过智能手机执行的预测的灵敏度和特异性是使用从训练中省去的60张图像计算的。
结果：已建立的AI对恶性病变显示出100％的敏感性和75％的特异性，并且每次使用AI服务器进行预测需要0.2s。使用智能手机进行预测需要2s，并且对恶性病变显示出100％的敏感性和97.5％的特异性。
结论：使用AI服务器获得了良好的质量预测。此外，通过智能手机进行预测的质量略好于人工智能服务器，可以安全，廉价地引入HIS。
