Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. Due to its speed and cost-efficiency, manual microscopy of auramine-stained sputum smears remains a crucial first-line detection method. However, it puts considerable workload on laboratory staff and suffers from a limited sensitivity. Here we validate a scanning and analysis system that combines fully-automated microscopy with deep-learning based image analysis. After automated scanning, the system summarizes diagnosis-relevant image information and presents it to the microbiologist in order to assist diagnosis. We tested the benefit of the automated scanning and analysis system using 531 slides from routine workflow, of which 56 were from culture positive specimen. Assistance by the scanning and analysis system allowed for a higher sensitivity (40/56 positive slides detected) than manual microscopy (34/56 positive slides detected), while greatly reducing manual slide-analysis time from a recommended 5-15 min to around 10 s per slide on average.

中文翻译：

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金胺染色的机器辅助解释显着提高了显微镜下结核病诊断的通量和灵敏度

在所有细菌传染病中，结核分枝杆菌感染是全世界人类发病率和死亡率最高的疾病之一。由于其速度和成本效益，金胺染色痰涂片的手动显微镜检查仍然是至关重要的一线检测方法。然而，它给实验室工作人员带来了相当大的工作量，并且灵敏度有限。在这里，我们验证了一个扫描和分析系统，该系统将全自动显微镜与基于深度学习的图像分析相结合。系统自动扫描后，汇总与诊断相关的图像信息，并呈现给微生物学家，以辅助诊断。我们使用来自常规工作流程的 531 张载玻片测试了自动扫描和分析系统的优势，其中 56 张来自培养阳性标本。