School of Industrial and Management Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, Korea.
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Sci Rep. 2022 Nov 9;12(1):19130. doi: 10.1038/s41598-022-21841-w.
The computer-aided diagnosis (CAD) for chest X-rays was developed more than 50 years ago. However, there are still unmet needs for its versatile use in our medical fields. We planned this study to develop a multipotent CAD model suitable for general use including in primary care areas. We planned this study to solve the problem by using computed tomography (CT) scan with its one-to-one matched chest X-ray dataset. The data was extracted and preprocessed by pulmonology experts by using the bounding boxes to locate lesions of interest. For detecting multiple lesions, multi-object detection by faster R-CNN and by RetinaNet was adopted and compared. A total of twelve diagnostic labels were defined as the followings: pleural effusion, atelectasis, pulmonary nodule, cardiomegaly, consolidation, emphysema, pneumothorax, chemo-port, bronchial wall thickening, reticular opacity, pleural thickening, and bronchiectasis. The Faster R-CNN model showed higher overall sensitivity than RetinaNet, nevertheless the values of specificity were opposite. Some values such as cardiomegaly and chemo-port showed excellent sensitivity (100.0%, both). Others showed that the unique results such as bronchial wall thickening, reticular opacity, and pleural thickening can be described in the chest area. As far as we know, this is the first study to develop an object detection model for chest X-rays based on chest area defined by CT scans in one-to-one matched manner, preprocessed and conducted by a group of experts in pulmonology. Our model can be a potential tool for detecting the whole chest area with multiple diagnoses from a simple X-ray that is routinely taken in most clinics and hospitals on daily basis.
计算机辅助诊断（CAD）在胸部 X 光片上的应用可以追溯到 50 多年前。然而，在我们的医学领域中，它的广泛应用仍然存在尚未满足的需求。我们计划开展这项研究，旨在开发一种多功能 CAD 模型，使其能够适用于包括初级保健领域在内的一般用途。我们计划通过使用计算机断层扫描（CT）与一对一匹配的胸部 X 射线数据集来解决这个问题。数据由肺病专家使用边界框提取和预处理，以定位感兴趣的病变。为了检测多个病变，我们采用了更快的 R-CNN 和 RetinaNet 进行多目标检测，并进行了比较。总共定义了十二个诊断标签，如下所示：胸腔积液、肺不张、肺结节、心脏扩大、实变、肺气肿、气胸、化疗端口、支气管壁增厚、网状混浊、胸膜增厚和支气管扩张。Faster R-CNN 模型的整体敏感性高于 RetinaNet，但特异性值相反。一些值，如心脏扩大和化疗端口，显示出很高的敏感性（均为 100.0%）。其他值，如支气管壁增厚、网状混浊和胸膜增厚，可以在胸部区域描述。据我们所知，这是第一项基于 CT 扫描一对一匹配、由一组肺病专家预处理和进行的胸部区域对象检测模型的研究。我们的模型可以成为一种从日常大多数诊所和医院常规拍摄的简单 X 光片中，对整个胸部区域进行多种诊断的潜在工具。
