COVID-19 infection is currently confirmed by a complex, multiple-step procedure starting with a mucosal swab, followed by viral RNA extraction and processing and qPCR. This study aims to explore a novel method using machine learning and artificial intelligence (AI) algorithm to diagnose COVID-19 infection through the morphological analysis of lymphocyte subset in the peripheral blood. This study will also risk stratify patients with COVID 19 infection based on the above finding along with other clinical, haematological and biochemical parameters with a view to predict clinical outcome with high sensitivity and specificity.
This is an observational study which will be carried out at East Suffolk and North Essex NHS
Foundation Trust (ESNEFT) in collaboration with University of Suffolk (UoS).
Investigators aim to analyse subsets of lymphocytes in the prospective blood smear slides
using machine learning and AI algorithm obtained from participants with a positive qPCR test
for COVID-19 who have required a hospital admission. The control group will consist of
archived blood smear slide data from patients both with i) non-suspected viral infections,
and ii) those with a non-COVID-19 viral infection obtained prior to the emergence of COVID-19
infection in the United Kingdom. In total, 785 blood smear slides will be analysed. The aim
of this study is to establish the diagnosis of COVID 19 infection based on lymphocyte
morphology on patients with COVID-19 infection from other patients with non COVID -19 viral
infections. A high definition single cell lymphocyte image from patients with COVID 19
infection and control group will be analysed using open source histopathology imaging
software CellProfiler against very fine cytoplasmic and nuclear details of the cells through
supervised and unsupervised machine learning algorithm to identify recurring pattern that is
unique to COVID 19 infection. The study will also assess other relevant clinical,
haematological and biochemical parameters in conjunction with the above morphological
features to develop a risk stratification tool to predict the clinical outcome of patients
with COVID-19 infection with high specificity and sensitivity using bioinformatics pipeline.
Inclusion Criteria:
- Female or male participants
- Aged over 18 years old (no upper age limit)
- Patients with SARS-COV-2 positive diagnosis based on qPCR (Study COVID 19 group)
- Peripheral blood smear slides from patients with no viral infection, reposited in the
laboratory slides archive within the facility prior to the emergence of COVID-19
infection in the United Kingdom (Control group)
- Peripheral blood smear slides from patients with a non-SARS-CoV-2 viral infection that
were reposited in the laboratory slides archive within the facility prior to the
emergence of COVID-19 infection in the United Kingdom (Control group).
Exclusion Criteria:
- Patients that are less than 18 years old
- Patients with SARS-COV-2 negative diagnosis based on qPCRPatients who have been
haematological malignancies with lymphocytosis as predominant manifestation.
- Patients who have lymphopenia in the past due to underlying inflammatory disorders.
- Patients who have lymphopenia due to previous cytotoxic or immunosuppressive therapy.
- Positive diagnosis of Human Immunodeficiency Virus (HIV).
East Suffolk and North Essex NHS Foundation Trust
Ipswich, United Kingdom
Mahesh Prahladan, Principal Investigator
East Suffolk and North Essex NHS Foundation Trust