COVID-19 Infection and Machine Learning Using Artificial Intelligence (AI)

Rapid Diagnosis of COVID-19 Positive Patients With Artificial Intelligence (AI) Algorithm Using Clinical and Image Analytical Parameters to Evaluate the Lymphocyte Subsets in the Peripheral Blood

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).