Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT.

BACKGROUND: Even though antibiotics agents are widely used, pneumonia is still one of the most common causes of death around the world. Some severe, fast-spreading pneumonia can even cause huge influence on global economy and life security. In order to give optimal medication regimens and prevent infectious pneumonia's spreading, recognition of pathogens is important. METHOD: In this single-institution retrospective study, 2,353 patients with their CT volumes are included, each of whom was infected by one of 12 known kinds of pathogens. We propose Deep Diagnostic Agent Forest (DDAF) to recognize the pathogen of a patient based on ones' CT volume, which is a challenging multiclass classification problem, with large intraclass variations and small interclass variations and very imbalanced data. RESULTS: The model achieves 0.899 ± 0.004 multi-way area under curves of receiver (AUC) for level-I pathogen recognition, which are five rough groups of pathogens, and 0.851 ± 0.003 AUC for level-II recognition, which are 12 fine-level pathogens. The model also outperforms the average result of seven human readers in level-I recognition and outperforms all readers in level-II recognition, who can only reach an average result of 7.71 ± 4.10% accuracy. CONCLUSION: Deep learning model can help in recognition pathogens using CTs only, which might help accelerate the process of etiological diagnosis.

Did patients with COVID-19 receive timely treatment in the early epidemic?-a systematic review and meta-analysis.

BACKGROUND: Corona virus disease 2019 (COVID-19) showed a significant difference in case fatality rate between different regions at the early stage of the epidemic. In addition to the well-known factors such as age structure, detection efficiency, and race, there was also a possibility that medical resource shortage caused the increase of the case fatality rate in some regions. METHODS: Medline, Cochrane Library, Embase, Web of Science, CBM, CNKI, and Wanfang of identified articles were searched through 29 June 2020. Cohort studies and case series with duration information on COVID-19 patients were included. Two independent reviewers extracted the data using a standardized data collection form and assessed the risk of bias. Data were synthesized through description and analysis methods including a meta-analysis. RESULTS: A total of 109 articles were retrieved. The time interval from onset to the first medical visit of COVID-19 patients in China was 3.38±1.55 days (corresponding intervals in Hubei province, non-Hubei provinces, Wuhan, Hubei provinces without Wuhan were 4.22±1.13, 3.10±1.57, 4.20±0.97, and 4.34±1.72 days, respectively). The time interval from onset to the hospitalization of COVID-19 patients in China was 8.35±6.83 days (same corresponding intervals were 12.94±7.43, 4.17±1.45, 14.86±7.12, and 5.36±1.19 days, respectively), and when it was outside China, this interval was 5.27±1.19 days. DISCUSSION: In the early stage of the COVID-19 epidemic, patients with COVID-19 did not receive timely treatment, resulting in a higher case fatality rate in Hubei province, partly due to the relatively insufficient and unequal medical resources. This research suggested that additional deaths caused by the out-of-control epidemic can be avoided if prevention and control work is carried out at the early stage of the epidemic. TRIAL REGISTRATION: CRD42020195606.

A rapid screening classifier for diagnosing COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .