Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-Ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. The AI model achieves COVID-19 sensitivity of 89.5% +\- 0.11, CAP sensitivity of 95% +\- 0.11, normal cases sensitivity (specificity) of 85.7% +\- 0.16, and accuracy of 90% +\- 0.06. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of 94.3% +\- pm 0.05, CAP sensitivity of 96.7% +\- 0.07, normal cases sensitivity (specificity) of 91% +\- 0.09 , and accuracy of 94.1% +\- 0.03. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

翻译：抗逆转录酶链反应(RT-PCR)目前是COVID-19诊断中0-肝脏反应(RT-PCR)的黄金敏感度标准。然而,提供诊断需要数天才能提供诊断,而虚假的负率相对较高。成像,特别是胸部计算透析(CT),可以帮助诊断和评估这一疾病。然而,据显示,标准剂量CT扫描给病人,特别是需要多次扫描的病人,带来了严重的辐射负担。在本研究中,我们认为低剂量和超低剂量(LDCT和ULDT)扫描程序可以使辐射暴露接近于0-Ray的0-肝脏敏感度。但是,需要花几天才能提供诊断诊断,而假说,低剂量和超低剂量(LDCT和ULDD)的准确性能接近0-Ray,同时为诊断目的维持一个可接受的分辨率解析。由于在大流行病期间可能无法广泛获得的心智学知识,我们开发了一个人工智能情报(AI)框架框架,使用收集的数据集(LTRT/ULDC) 951的正常的内分机数据。我们提出的模型可以提供人的性性反应的性反应。