For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

/pdf/artificial-intelligence-enabled-rapid-diagnosis-of-patients-3zu3m2c4xy.pdf

Artificial intelligence-enabled rapid diagnosis of patients with COVID-19.

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image analysis. The lungs, bone, and mediastinum were included in terms of anatomy, while X-ray and computed tomography (CT), with and without contrast enhancement, were considered regarding imaging modalities. Four key components of deep learning were summarized, namely, network architectures, learning strategies, optimization methods, and vision tasks. Disease-specific applications were discussed in detail with respect to the dimension of the data input, network architecture, and modality: lung cancer, pneumonia, tuberculosis, pulmonary embolism, chronic obstructive pulmonary disease, and interstitial lung disease for lung; traumatic fractures, osteoporosis, osteoporotic fractures, and bone metastases for bone; and coronary artery calcification and aortic dissection for vascular diseases. Finally, five promising research directions and possible solutions were presented for future work.

Recent developments in the application of artificial intelligence to chest medical image analysis

COVID-19 Classification Using CT Scans with Convolutional Neural Networks

For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

/pdf/artificial-intelligence-enabled-rapid-diagnosis-of-covid-19-ndl42a55bu.pdf

Artificial intelligence-enabled rapid diagnosis of COVID-19 patients.

This literature review summarizes the current deep learning methods developed by the medical imaging AI research community that have been focused on resolving lung imaging problems related to coronavirus disease 2019 (COVID-19). COVID-19 shares many of the same imaging characteristics as other common forms of bacterial and viral pneumonia. Differentiating COVID-19 from other common pulmonary infections is a non-trivial task. To help offset what commonly requires hours of tedious manual annotation, several innovative solutions have been published to help healthcare providers during the COVID-19 pandemic. However, the absence of a comprehensive survey on the subject makes it challenging to ascertain which approaches are promising and therefore deserve further investigation. In this survey, we present an in-depth review of deep learning techniques that have recently been applied to the task of discovering the diagnosis and prognosis of COVID-19 patients. We categorize existing approaches based on features such as dimensionality of radiological imaging, system purpose, and used deep learning techniques, underlying core issues, and challenges. We also address the merits and shortcomings of various approaches, and finally we discuss future directions for this research.

Deep Learning Techniques for COVID-19 Diagnosis and Prognosis Based on Radiological Imaging

Background Pulmonary tuberculosis (PTB) is a global health problem and remains the leading infectious cause of death worldwide. Differentiation between secondary PTB and non-tuberculous (non-TB) pneumonia is important for patient isolation and treatment, but can be difficult to determine clinically and radiologically. We proposed the application of deep learning to chest computerized tomography (CT) to assist doctors in detecting and differentiating PTB from non-TB pneumonia in an expedient, non-invasive, and reproducible manner.

Methods: We retrospectively collected a dataset containing 1,124 CT scans from 923 PTB and non-TB pneumonia based on their pathological reports of lung biopsy and clinical information and 201 patients without pulmonary infiltrate from West China Hospital between 2012 and 2018. Randomly selected parts of this dataset (WCPR dataset) were used to develop, train, internally validate and test the algorithm. Patients in the WCPR dataset (PTB, n=439; non-TB pneumonia, n=484; normal, n= 201) were randomly assigned in three non-overlapping sets: training set, n=866; validation set, n=108; and test set, n=150. An additional dataset from NIH TB Portal31 comprising of cases from three countries (Belarus, n = 274; Romania, n = 43; Moldova, n = 10) was used to validate externally the algorithm's ability to identify PTB. A convolutional neural network of Inception-Res-Net-v230 was trained and tested on the entire chest CT to mimic real life application. The performance of our algorithm was compared to three trained radiology/pulmonology physicians.

Findings For differentiating pulmonary infiltrates, the algorithm achieved 99·3% accuracy (149 out of 150), 99.0% sensitivity, and 100·0% specificity. For identifying PTB, the algorithm achieved 82·0% accuracy (123 out of 150), 95·9% sensitivity, and 75·2% specificity. For identifying non-TB pneumonia, the algorithm achieved 81·3% accuracy (122 out of 150), 52·7% sensitivity, and 97·9% specificity. This mostly outperformed our human readers for PTB identification, who averaged up to 81·1% accuracy, 70·8% sensitivity, and 86·1% specificity. Our algorithm identified 287 out of 327 PTB (87·8% accuracy) cases in NIH TB Portal Dataset from other countries.

Interpretation: Our deep-learning-based algorithm successfully differentiated abnormal from normal chest CTs, as well as PTB from non-TB pneumonia cases and thus allows real world applicability. Early identification of PTB from non-TB pneumonia can help control outbreaks through isolation and early appropriate treatment. The application of our algorithm could expedite the identification of PTB, thereby improving clinical outcomes. Our datasets and algorithm used in this study will be publicly available to facilitate world-wide adoption.

Funding Statement: The authors declare: "None."

Declaration of Interests: The authors declare: "None."

Ethics Approval Statement: This study was approved by the Institutional Review Board of West China Hospital (approval No. 2019-148) and Icahn School of Medicine at Mount Sinai (approval No. GCO#1: 19-0569(0001) ISMMS), and the patients’ written consents were waived.

A Generalized Deep Learning Approach for Evaluating Secondary Pulmonary Tuberculosis on Chest Computed Tomography

This article outlines the need for, and possible solutions to, the problem of third parties’ legally untrammelled ability to collect and use identified or identifiable personal, sometimes very sensitive, health data. The “datafication” of health information about individuals collected, managed, and disseminated without their knowledge and informed consent effectively treats data subjects – us – as mere means to an end. The article examines jurisprudential and technical proposals for safeguarding privacy of data subjects, and then critically analyses the European Union’s General Data Protection Regulation 2016/679 on the “protection of natural persons with regard to the processing of personal data and on the free movement of such data”; the United Kingdom’s approach to statutory controls on privacy-intrusive powers of national security and law enforcement agencies in in form of proportionality and necessity tests implemented under the Investigative Powers Act (UK) 2016; and the United States’ preference for integrated government–private–sector cybersecurity systems that safeguard data subjects’ rights to privacy and security by targeting specific Big Data and the Internet of Things (IoT) sectors. 
The article outlines proposal that envisages a four-tier legal framework for protection of identifiable personal health data. Protections at each tier would be discrete yet capable of integration – from operationalising data privacy and ethics by design at the level computer software (algorithmic) instructions to national legislation and international treaties.

Legal Protections for Personal Health Information in the Age of Big Data – A Proposal for Regulatory Framework

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A Generalized Deep Learning Approach for Evaluating Secondary Pulmonary Tuberculosis on Chest Computed Tomography

Legal Protections for Personal Health Information in the Age of Big Data – A Proposal for Regulatory Framework