OBJECTIVE:Diagnosis of tuberculosis (TB) on multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated detection system based on artificial intelligence (AI)in this study to simplify the diagnostic process of active tuberculosis (ATB) and improve the diagnostic accuracy using CT images. DATA:A CT image dataset of 846 patients is retrospectively collected from a large teaching hospital. The gold standard for ATB patients is sputum smear, and the gold standard for normal and pneumonia patients is the CT report result. The dataset is divided into independent training and testing data subsets. The training data contains 337 ATB, 110 pneumonia, and 120 normal cases, while the testing data contains 139 ATB, 40 pneumonia, and 100 normal cases, respectively. METHODS:A U-Net deep learning algorithm was applied for automatic detection and segmentation of ATB lesions. Image processing methods are then applied to CT layers diagnosed as ATB lesions by U-Net, which can detect potentially misdiagnosed layers, and can turn 2D ATB lesions into 3D lesions based on the consecutive existence of U-Net annotations. Finally, independent test data is used to evaluate the performance of the developed AI tool. RESULTS:For an independent test, the AI tool yields an AUC value of 0.980. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 0.968, 0.971, 0.971, 0.971, and 0.964, respectively, which shows that the AI tool performs well for detection of ATB and differential diagnosis of non-ATB (i.e. pneumonia and normal cases). CONCLUSION:An AI tool for automatic detection of ATB in chest CT is successfully developed in this study. The AI tool can accurately detect ATB patients, and distinguish between ATB and non- ATB cases, which simplifies the diagnosis process and lays a solid foundation for the next step of AI in CT diagnosis of ATB in clinical application.

中文翻译：

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基于深度学习的多层螺旋CT影像自动检测活动性肺结核的开发与验证[J].

目的：在许多缺乏经验丰富的放射科医生的结核病流行地区，在多层螺旋计算机断层扫描 (CT) 图像上诊断结核病 (TB) 是一项艰巨的任务。为了解决这一难题，我们在本研究中开发了一种基于人工智能 (AI) 的自动检测系统，以简化活动性结核病 (ATB) 的诊断过程并提高使用 CT 图像的诊断准确性。数据：回顾性收集了一家大型教学医院的 846 名患者的 CT 图像数据集。ATB患者的金标准是痰涂片，正常和肺炎患者的金标准是CT报告结果。数据集分为独立的训练和测试数据子集。训练数据包含 337 个 ATB、110 个肺炎和 120 个正常病例，而测试数据包含 139 个 ATB，分别为40例肺炎和100例正常病例。方法：应用U-Net深度学习算法对ATB病灶进行自动检测和分割。然后将图像处理方法应用于 U-Net 诊断为 ATB 病变的 CT 层，它可以检测潜在的误诊层，并可以根据 U-Net 注释的连续存在将 2D ATB 病变转化为 3D 病变。最后，使用独立的测试数据来评估开发的 AI 工具的性能。结果：对于独立测试，AI 工具产生的 AUC 值为 0.980。准确度、灵敏度、特异度、阳性预测值、阴性预测值分别为0.968、0.971、0.971、0.971、0.964，说明AI工具在ATB检测和非ATB（即肺炎）鉴别诊断方面表现良好和正常情况）。结论:本研究成功开发了一种用于自动检测胸部CT中ATB的AI工具。AI工具可以准确检测ATB患者，区分ATB和非ATB病例，简化了诊断流程，为下一步AI在ATB CT诊断中的临床应用奠定了坚实的基础。