Artificial Intelligence-assisted Diagnosis and Prognostication in COVID-19 Using Electrocardiograms and Imaging
Conditions: Coronavirus
Sponsor: Imperial College London

Joint Research  
Compliance Office 
 
AI-COV-19 Protocol 
V1.2, 5 May 2020  
 
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IRAS project ID: 283210   
AI-COV-19 
 
 
 
Artificial intelligence-assisted diagnosis and prognostication in 
COVID-19 using electrocardiograms and imaging (AI-COV-19) 
 
 
 
 
 
V1.2 05-05-2020 
 
 
 
 
MAIN SPONSOR: Imperial College London 
FUNDERS: N/A 
STUDY COORDINATION CENTRE: xxx 
 
IRAS Project ID: 283210 
REC reference: xxx 
 
 
 
Protocol authorised by: 
 
Name & Role 
Date 
Signature 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Study Management Group 

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V1.2, 5 May 2020  
 
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IRAS project ID: 283210   
 
Chief Investigator: Dr Fu Siong Ng  
Co-investigators: Dr Emmanuel Ako, Dr Abi Al-Hussaini; Dr Sadia Khan; Dr Jaymin Shah; Dr Kiran 
Patel; Professor Nicholas Peters 
 
Statistician: Dr Xinyang Li 
 
Study Management: Dr Fu Siong Ng 
 
 
Study Coordination Centre – Hammersmith Hospital 
 
For general queries, supply of study documentation, and collection of data, please contact: 
 
Study Coordinator: Kiran Patel 
Address: 4th Floor ICTEM building, Hammersmith campus, Imperial College London, 72 Du Cane 
Road, W12 0UQ 
 
 
 
 
 
 
Registration:       
Tel:        
 
 
 
 
 
 
E-mail:  kiran.patel@imperial.ac.uk 
Fax:        
 
 
 
 
 
 
Web address:       
 
 
Clinical Queries 
 
Clinical queries should be directed to Dr Fu Siong Ng who will direct the query to the appropriate 
person 
 
 
Sponsor 
 
Imperial College London is the main research Sponsor for this study.  For further information 
regarding the sponsorship conditions, please contact the Head of Regulatory Compliance at: 
 
 
 
Joint Research Compliance Office 
Imperial College London and Imperial College Healthcare NHS Trust 
Room 215, Level 2, Medical School Building 
Norfolk Place 
London, W2 1PG 
Tel: 0207 594 9459/ 0207 594 1862 
http://www3.imperial.ac.uk/clinicalresearchgovernanceoffice  
 
Funder 
 
This study is not funded from a specific research grant, and the researchers involved are funded 
through Imperial College London and Chelsea & Westminster Hospital NHS Foundation Trust. 
 
This protocol describes the Artificial intelligence-assisted diagnosis and prognostication in COVID-19 using 
electrocardiograms and imaging (AI-COV-19) study and provides information about procedures for entering 
participants.  Every care was taken in its drafting, but corrections or amendments may be necessary. These 

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AI-COV-19 Protocol 
V1.2, 5 May 2020  
 
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IRAS project ID: 283210   
will be circulated to investigators in the study.  Problems relating to this study should be referred, in the first 
instance, to the Chief Investigator.  
 
This study will adhere to the principles outlined in the UK Policy Frame Work for Health and Social Care 
Research. It will be conducted in compliance with the protocol, the Data Protection Act and other regulatory 
requirements as appropriate.  
 
 
 
Table of Contents                                                              
 Page No 
1. 
INTRODUCTION 
6 
1.1 BACKGROUND 
6 
2.  STUDY OBJECTIVES 
7 
3.  STUDY DESIGN 
8 
3.1 STUDY OUTCOME MEASURES 
8 
4. 
PARTICIPANT ENTRY 
8 
4.1 PRE-REGISTRATION EVALUATIONS 
8 
4.2 INCLUSION CRITERIA 
9 
4.3 EXCLUSION CRITERIA 
9 
4.4 WITHDRAWAL CRITERIA 
10 
5. 
ASSESSMENT AND FOLLOW-UP 
10 
6. 
STATISTICS AND DATA ANALYSIS 
10 
7. 
REGULATORY ISSUES 
10 
7.1 ETHICS APPROVAL 
10 
7.2 CONFIDENTIALITY 
11 
7.3 INDEMNITY 
11 
7.4 SPONSOR 
11 
7.5 FUNDING 
11 
7.6 AUDITS 
11 
8. 
STUDY MANAGEMENT 
11 
9. 
PUBLICATION POLICY 
11 
10. 
REFERENCES 
12 
 

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V1.2, 5 May 2020  
 
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GLOSSARY OF ABBREVIATIONS 
Abbreviation 
Definition 
ECG 
Electrocardiogram 
AI 
Artificial intelligence 
CXR 
Chest x ray 
CT 
Computed Tomography 
ML 
Machine learning 
MDT 
Multi-Disciplinary Team 
NHS 
National Health Service 
ROI 
Region of Interest 
CMIH 
Centre for Mathematical Imaging in Healthcare 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
KEYWORDS 
ECG, COVID-19, CT, chest x-ray, deep learning, artificial intelligence 
 
 
 
 

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STUDY SUMMARY 
 
TITLE 
AI-assisted diagnosis and prognostication in COVID-19 
DESIGN Retrospective data study on patients with suspicious and confirmed COVID-19. 
 
AIMS To investigate whether computer-based analysis (machine learning) enable the 
diagnosis of Covid-19 primarily on ECGs, but also from CXR and CT images 
 
OUTCOME MEASURES Accuracy with which computer based analysis (machine learning) can diagnose 
and/or prognosticate Covid-19 infection primarily from ECG, but also from chest 
xray and CT images 
POPULATION The study aims to collect data from up to 2000 patients at Chelsea and 
Westminster Hospital NHS Foundation Trust, Imperial College Healthcare NHS 
Trust and London North West London University Healthcare NHS Trust, from 15 
May 2020.  
 
ELIGIBILITY To be included in this study the patient must: 
- 
have ECGs, Chest x-ray and/or chest CT imaging (with or without 
contrast) 
- 
laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat 
swab samples) or clinical suspicion for Covid-19 infection 
- 
be aged >18 years 
 
DURATION 2 years 
 
 
 
 
 
 

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1. 
INTRODUCTION  
 
1.1 
BACKGROUND 
Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019 [1] 
[2]. It is highly contagious, and severe cases can lead to acute respiratory distress or multiple 
organ failure [3]. On 11 March 2020, the WHO made the assessment that COVID-19 can be 
characterised as a pandemic. As of 25 March 2020, in total, 416, 916 cases of COVID-19 have 
been recorded, and the death toll has reached 18,565 with a rapid increase of cases in Europe 
and North America.  
 
The disease can be confirmed by using the reverse-transcription polymerase chain reaction (RT-
PCR) test [4]. While being the gold standard for diagnosis, confirming COVID-19 patients using 
RT-PCR is time-consuming, and both high false-negative rates and low sensitivities (60-95%) may 
put hurdles for the presumptive patients to be identified and treated early [3][5][6]. With infected 
patients and tested individuals steadily increasing, health care systems are overwhelmed by the 
unprecedented testing frequencies and massive patient loads. Thus, the situation presents a 
diagnostic dilemma with patients showing non-specific symptoms, as well as RT-PCR assays 
often being unavailable and known for high false negative rates. 
 
As non-invasive electrocardiographic (ECG) recordings and imaging techniques, such as 
computed tomography (CT) and chest X-ray (CXR) can detect those characteristics, e.g., 
myocarditis, bilateral patchy shadows or ground glass opacity (GGO), manifested in the COVID-19 
[7][8]. Hence ECG, CT and CXR may serve as an important tool for COVID-19 to be screened, 
diagnosed and prognosticated early. 
 
 
1.2 
RATIONALE FOR CURRENT STUDY 
 
Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. It 
is highly contagious, and severe cases can lead to acute respiratory distress or multiple organ 
failure and ultimately death. The disease can be confirmed by using the reverse-transcription 
polymerase chain reaction (RT-PCR) test. ECGs, Chest x-rays and CT scans are rich sources of 
data that provide insight to disease that otherwise would not be available. Knowing who to admit to 
the hospital or intensive care saves lives as it helps to mitigate resource shortages. Novel Artificial 
Intelligence tools such as Deep learning will allow a complex assessment of the Imaging and 
clinical data that could potentially help clinicians to make a faster and more accurate diagnosis, 
better triage patients and assess treatment response and ultimately better prediction of outcome.  
 
Our group has significant experience implementing machine learning algorithms on vast quantities 
of ECGs, such as from the UK Biobank, and propose to extend our techniques to data from 
patients with Covid-19. 
ECGs are routinely performed on unwell patients presented to hospital and often have serial 
recordings during their inpatient stay. This provides a unique method by which disease 
progression can be tracked using a simple, non-invasive and inexpensive method throughout the 
course of the illness. Furthermore, whether COVID-19 causes specific ECG changes is not known. 
Identifying and understanding ECG changes related to COVID-19, particularly those features that 
may predict clinical outcomes, would be especially useful. The non-invasive, quick and easy 
nature of recording ECGs in unwell patients makes it an ideal modality to investigate and predict 
COVID-19 related outcomes. 

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As the manifestation of COVID-19 and related complications on ECG is currently unknown, we 
hypothesise that beyond routine radiographic imaging, ECG can provide an insight into clinical 
outcomes related to COVID-19 that can be helpful for prognostication.  
Imaging of COVID-19 is performed with CXR and CT.  Chest radiograph is the initial screening 
imaging tool in patients with suspicious clinical symptoms of COVID-19 (including fever and 
lymphopenia). Radiographic appearances are of symmetric patchy or diffuse airspace opacities. 
Typical CT appearances include peripheral, bilateral ground glass opacity (GGO) which may have 
a rounded morphology, with or without consolidation. A crazy-paving pattern can be seen with 
ground glass opacity and intralobular lines. Imaging features depend on when infected patients 
are imaged. Up to approximately 50% of patients with COVID-19 infection may have normal CT 
scans 0–2 days after onset of flu-like symptoms [18]. GGO opacity usually develops between day 
0 and 4 after symptom onset. The frequency of consolidation increases later in the course of the 
disease, peaking at 6-13 days with higher rates of bilateral and multilobar involvement. An 
organising pneumonia pattern is observed with reverse halo or atoll sign and perilobular opacities. 
About 2–3 weeks after the onset, CT may show varying degrees of clearing of the patchy 
consolidative opacities, reticular opacities, bronchial wall thickening, and interlobular septal 
thickening [9]. Imaging (CXR & CT) may be used as an aid in diagnosing COVID-19, guide 
individual patient management decisions, deal with complications or look for an alternative 
diagnosis. 
With the development of machine learning, deep learning based artificial intelligence (AI) 
technology has demonstrated tremendous success in the field of medical data analysis due to its 
capacity of extracting rich features from imaging and complex clinical datasets [10]. Recently, 
deep learning has been used for diagnosing and distinguishing bacterial and viral pneumonia from 
thoracic imaging data [11]. In addition, attempts have been made to detect various ECG and chest 
CT imaging features [12]. In the current COVID-19 pandemic, some preliminary studies have 
shown promising results for the ECG, chest CT and CXR data analysis and classification 
[2][3][13]. Besides, small scale initial studies have been done for COVID-19 monitoring [14], 
screening [15] and prediction of the hospital stay [16]. 
 
Exploring the added value of artificial intelligence-assisted disease detection and characterisation 
may potentially help clinicians to make a prompt correct diagnosis, guide treatment and predict 
prognosis. 
 
We hypothesise ECGs, CXR and CT scans from patients with confirmed or suspected COVID-19 
infection can be used to develop machine learning algorithms that will subsequently diagnose and 
predict the clinical course of the infection in other patients as well. Furthermore, we believe 
machine learning and artificial intelligence when applied to ECG, CXR and CT can evaluate the 
effect of COVID-19 on heart function and more accurately diagnose and prognosticate infection 
than doctors. 
 
 
2.  STUDY OBJECTIVES 
 
Primary objectives 
● We will investigate if the standard electrocardiogram (ECG), which is a conventional clinical 
tool used record the electrical activity of the heart, a chest X-ray and/or a CT scan, can be 
used to predict the course and severity of disease in patients with Coronavirus Disease 
2019 (COVID-19).  
Secondary objectives 

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● We will also investigate if the standard electrocardiogram (ECG), which is a conventional 
clinical tool used record the electrical activity of the heart, a chest X-ray or a CT scan, can 
be used in patients with Coronavirus Disease 2019 (COVID-19) to predict subsequent 
heart involvement due to the infection. 
 
● We will assess if computer-based analyses  are better than doctors at diagnosing features 
of COVID-19 infection. 
 
 
3.  STUDY DESIGN 
 
This is a retrospective observational study on patients with suspicious and confirmed COVID-19. 
 
The study aims to recruit up to 2000 patients who will be referred to have ECGs, chest X-rays or 
CT scans at Chelsea and Westminster Hospital NHS Foundation Trust, Imperial College 
Healthcare NHS Trust and London North West London University Healthcare NHS Trust, since 1 
February 2020.  
 
Patients with suboptimal ECGs, chest radiograph and CT studies due to artefacts will be excluded. 
 
The study will gather data retrospective and therefore data collection will cease once the 
prespecified number of participants is reached. 
 
3.1 
STUDY OUTCOME MEASURES 
The  accuracy with which computer based analysis (machine learning) can diagnose and/or 
prognosticate Covid-19 infection primarily from ECG, but also from chest xray and CT images. 
 
To determine if computer based analysis (deep learning) enables assessment of the effect of 
COVID-19 on heart function. 
 
To determine if AI system is superior to cardiology/medical team and/or radiologist assessment. 
 
 
 
 
4. 
PARTICIPANT ENTRY  
 
4.1 
PRE-REGISTRATION EVALUATIONS  
Clinical data will be reviewed by a member of the clinical team and the following clinical and 
laboratory data will be recorded:  
 
● Gender 
● Age 
● Height/Weight, body mass index 
 
● Medical history (diabetes, hypertension, coronary heart disease, malignancy, structural 
lung disease, pulmonary heart disease, chronic liver disease, immunosuppressed 
population) 
 
● The duration of symptoms at the first visit (days, could be inquired from the main complaint 
of the patient at the first visit, symptoms including fever, dry cough, headache, myalgia, 
weakness, etc.) 

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● Smoking status (Never = 0, Ex-smoker = 1, Current = 2, Unknown = 3) 
● Pack Years History  
● Current NSAID use 
● Current ACEi use 
● Current Angiotensin receptor blocker use  
● Respiratory rate (breaths/min) 
 
● Heart Rate 
● Blood Pressure 
 
● Temperature  
● Clinical Classiﬁcation   
● oxygen saturation at a rest state (%)  
● arterial partial pressure of oxygen (PaO2)/oxygen concentration (FiO2, mmHg) 
● Recovery/Death 
 
● Time/date of the CT scanning 
 
● Time/date of the Chest x ray 
● D-dimer 
● White blood cell count 
● Lymphocyte cell count 
● platelet count 
● C-reactive protein  
 
● Troponin T 
 
● ALT, AST, ALB, ESR, CD3, CD3 count, CD4, CD4 count, CD8, CD8 count, CD19, CD19 
count, IL-6, IL-8, IL-10, TNF-γ 
● RT-PCR results for SARS-Cov 2 (Date, results) 
● Stay at ICU (days) 
● Hospital Stay (days) 
● Date or death  
● Cause of death  
● Intubation  
● Date of Intubation  
● Time mechanical Ventilation (days) 
● NEWS score on arrival 
 
4.2 
INCLUSION CRITERIA 
To be included in this study the patient must: 
- 
have ECGs, Chest x-ray and/or chest CT imaging (with or without contrast)  
- 
positive laboratory Covid-19 virus nucleic acid test (RTPCR assay with throat swab 
samples) or clinical suspicion for Covid-19 infection 
- 
be aged >18 years 
 
4.3 
EXCLUSION CRITERIA 
The presence of any of the following will preclude the patient from the study: 
 

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1. 
Suboptimal ECGs, chest radiographs or CT studies for deep learning methods due to 
artefacts including severe motion artefacts which causes blurring of the contours of or 
significant artefacts due to metallic prosthesis which causes image degradation 
2. 
Time-interval between ECGs, chest CT and the RT-PCR assay was longer than 7 days 
 
4.4 
WITHDRAWAL CRITERIA 
This is a retrospective study and therefore there are no withdrawal criteria, in addition to the 
inclusion and exclusion criteria. 
 
 
 
5. 
ASSESSMENT AND FOLLOW-UP  
 
This is a retrospective data study and therefore there will be no follow-up. Therefore, there will be 
no assessments. 
 
As all clinical data will be anonymised, incidental findings will not be reviewed or reported back to 
individuals or their GP or clinical care teams. 
 
The study will end when the last pseudo-anonymised image has been segmented and ECG 
feature extracted, and the deep learning algorithm has been trained and validated.  
 
 
6. 
STATISTICS AND DATA ANALYSIS 
 
Machine learning algorithms will be trained to differentiate between patient groups (e.g. based on 
diagnosis or prognosis). For example, for the ECG analyses, in addition to conventional 
morphological features of QRS, we propose to analyse both temporal and spectrum features of 
the 12-lead ECG. The spectrum features would include band-powers of different frequency bands, 
and coefficients of auto-regression (AR) models of different orders would be used as temporal 
features. Moreover, the temporal stability of features (i.e., how the features vary from beat to beat) 
and the spatial dispersion of these features (i.e., who the features vary across the 12 leads) will 
also be investigated. 
 
Data will be analysed by the chief investigator and the research team at the National Heart and 
Lung Institute, Imperial College London. Anonymised data may be shared with collaborators, for 
example at University College London, for data processing subject to any required data sharing 
agreements/approval being in place before the data is transferred. 
 
Data and all appropriate documentation will be stored for a minimum of 10 years after the 
completion of the study, including the follow-up period.   
 
7. 
REGULATORY ISSUES 
 
7.1 
ETHICS APPROVAL 
The Study Coordination Centre has obtained approval from Health Regulator Authority (HRA). The 
study must also receive confirmation of capacity and capability from each participating NHS Trust 
before accepting participants into the study or any research activity is carried out. The study will be 
conducted in accordance with the recommendations for physicians involved in research on human 
subjects adopted by the 18th World Medical Assembly, Helsinki 1964 and later revisions. 
 

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An approval from the National Research Ethics Committee (NRES) will be obtained before the 
start of any study related procedure and other relevant documents. All correspondence with the 
NRES will be retained in the Study Files. 
 
 
7.2 
CONFIDENTIALITY 
The Chief Investigator will preserve the confidentiality of participants taking part in the study and is 
registered under the Data Protection Act. 
 
7.3 
INDEMNITY 
Imperial College London holds negligent harm and non-negligent harm insurance policies which 
apply to this study. 
 
7.4 
SPONSOR 
Imperial College London will act as the main Sponsor for this study.  Delegated responsibilities will 
be assigned to the NHS trusts taking part in this study.   
 
7.5 
FUNDING 
This study is not funded from a specific research grant, and the researchers involved are funded 
through Imperial College London and Chelsea & Westminster Hospital NHS Foundation Trust. 
 
There will be no payments to research participants or researchers. 
 
7.6 
AUDITS  
The study may be subject to inspection and audit by Imperial College London under their remit as 
sponsor and other regulatory bodies to ensure adherence to GCP and the UK Policy Frame Work 
for Health and Social Care Research.  
 
 
8. 
STUDY MANAGEMENT 
 
The day-to-day management of the study will be co-ordinated through Imperial College London, 
Chelsea and Westminster Hospital NHS Foundation Trust and London North West London 
University Healthcare NHS Trust. 
 
The investigator will make all study documentation and related records available should an auditor 
inspection occur. Should a monitoring visit or audit be requested, the investigator will make the 
study documentation and source data available to the Sponsor’s representative. All patient data 
will be handled and treated confidentially   
 
9. 
PUBLICATION POLICY 
 
The investigators will publish the findings of the study as conference abstract(s) and in peer-
reviewed journals. 
 
 

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10. 
REFERENCES 
1. Chen, Jun, Lianlian Wu, Jun Zhang, Liang Zhang, Dexin Gong, Yilin Zhao, Shan Hu et al. 
"Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-
resolution computed tomography: a prospective study." medRxiv (2020). 
2. Shan, Fei, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, 
Dinggang Shen, and Yuxin Shi. "Lung Infection Quantification of COVID-19 in CT Images 
with Deep Learning." arXiv preprint arXiv:2003.04655 (2020). 
3. Li, Lin, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, Bin Kong, Junjie Bai et al. "Artificial 
Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT." 
Radiology (2020): 200905. 
4. Xie, Xingzhi, Zheng Zhong, Wei Zhao, Chao Zheng, Fei Wang, and Jun Liu. "Chest CT for 
typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing." Radiology (2020): 
200343. 
5. Ai, Tao, Zhenlu Yang, Hongyan Hou, Chenao Zhan, Chong Chen, Wenzhi Lv, Qian Tao, 
Ziyong Sun, and Liming Xia. "Correlation of chest CT and RT-PCR testing in coronavirus 
disease 2019 (COVID-19) in China: a report of 1014 cases." Radiology (2020): 200642. 
6. Fang, Yicheng, Huangqi Zhang, Jicheng Xie, Minjie Lin, Lingjun Ying, Peipei Pang, and 
Wenbin Ji. "Sensitivity of chest CT for COVID-19: comparison to RT-PCR." Radiology 
(2020): 200432. 
7. Huang, Chaolin, Yeming Wang, Xingwang Li, Lili Ren, Jianping Zhao, Yi Hu, Li Zhang et 
al. "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China." 
The Lancet 395, no. 10223 (2020): 497-506. 
8. Wang, Dawei, Bo Hu, Chang Hu, Fangfang Zhu, Xing Liu, Jing Zhang, Binbin Wang et al. 
"Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected 
pneumonia in Wuhan, China." Jama (2020).  
9. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 
Patients Sana Salehi, Aidin Abedi, Sudheer Balakrishnan, and Ali Gholamrezanezhad 
American Journal of Roentgenology 0 0:0, 1-7. 10.2214/AJR.20.23034 
10. Litjens, Geert, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, 
Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, 
and Clara I. Sánchez. "A survey on deep learning in medical image analysis." Medical 
image analysis 42 (2017): 60-88. 
11. Rajaraman, Sivaramakrishnan, Sema Candemir, Incheol Kim, George Thoma, and Sameer 
Antani. "Visualization and interpretation of convolutional neural network predictions in 
detecting pneumonia in pediatric chest radiographs." Applied Sciences 8, no. 10 (2018): 
1715. 
12. Anthimopoulos, Marios, Stergios Christodoulidis, Lukas Ebner, Andreas Christe, and 
Stavroula Mougiakakou. "Lung pattern classification for interstitial lung diseases using a 
deep convolutional neural network." IEEE transactions on medical imaging 35, no. 5 
(2016): 1207-1216. 

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13. Song, Ying, Shuangjia Zheng, Liang Li, Xiang Zhang, Xiaodong Zhang, Ziwang Huang, 
Jianwen Chen et al. "Deep learning Enables Accurate Diagnosis of Novel Coronavirus 
(COVID-19) with CT images." medRxiv (2020). 
14. Gozes, Ophir, Maayan Frid-Adar, Hayit Greenspan, Patrick D. Browning, Huangqi Zhang, 
Wenbin Ji, Adam Bernheim, and Eliot Siegel. "Rapid AI Development Cycle for the 
Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient 
Monitoring using Deep Learning CT Image Analysis." arXiv preprint arXiv:2003.05037 
(2020). 
15. Xu, Xiaowei, Xiangao Jiang, Chunlian Ma, Peng Du, Xukun Li, Shuangzhi Lv, Liang Yu et 
al. "Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia." arXiv 
preprint arXiv:2002.09334 (2020). 
16. Qi, Xiaolong, Zicheng Jiang, Qian Yu, Chuxiao Shao, Hongguang Zhang, Hongmei Yue, 
Baoyi Ma et al. "Machine learning-based CT radiomics model for predicting hospital stay in 
patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study." 
medRxiv (2020).