COVID-19NCT04756518
COVID-19 Infection and Machine Learning Using Artificial Intelligence (AI)
An observational study of COVID-19 and COVID, run by East Suffolk and North Essex NHS Foundation Trust at 1 site in United Kingdom.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- COVID-19, COVID, SARS-CoV 2
- How many people
- 215 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 6 Jul 2020to 31 Mar 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Peripheral blood smears obtained from adult non-SARS-CoV-2 positive patients and from adult positive SARS-CoV-2 patients.
Who may be able to join
- 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).
Who cannot join
- 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).
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
East Suffolk and North Essex NHS Foundation Trust
Ipswich, IP4 5PD
About this study
Official title: 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.
Read the detailed description
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.
- Conditions
- COVID-19, COVID, SARS-CoV 2
- Medical subject headings
- COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| COVID 19 group | The COVID 19 group will consist of peripheral blood smear slides from patients who are in the hospital who had qPCR results positive for COVID-19. | |
| CONTROL group | A control group will consist of i) peripheral blood smear slides from patients with no viral infection and ii) from those with a non-SARS-CoV-2 viral infection. Control group peripheral blood slides will be randomly selected from the laboratory slides archive within the facility. The laboratory slides used will be inclusive of slides archived prior to the emergence of COVID-19 infection in the United Kingdom. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosis of COVID-19.
Determine whether lymphocytes alone can diagnose COVID-19 disease with high specificity and sensitivity, using AI-based image analytical modelling.
Measured: 6 months
Secondary outcomes (1)
Severity of COVID-19 infection modelling
The secondary outcome measure of the study will be to create risk stratification modelling, to aid in predicting the severity and mortality of the infection, based on our above-mentioned, novel diagnostic tool and additional clinical, haematological and biochemical parameters; ensuring high specificity, with consequent facilitated management of patients both in a hospital and outpatient setting. The model proposed intends to use and evaluate the clinical parameters including oxygen saturation at the time of venesection, and other vital statistics, including: pulse, blood pressure and respiratory rate, along with other parameters such as LDH, ferritin, C-reactive protein (CRP), D-dimers, renal function, all together helping to predict disease outcome and severity.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Feb 2021
- Study start6 Jul 2020
- Primary completion31 Mar 2022
- Study completion31 Mar 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 215 participants (actual)
Sponsor and investigators
- Lead sponsor
- East Suffolk and North Essex NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- University of Suffolk
- Responsible party
- Sponsor
- Investigators
- Mahesh Prahladan, Principal investigator, East Suffolk and North Essex NHS Foundation Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04756518
- Sponsor's protocol ID
- 20/053
- Status last verified
- Feb 2023
- First submitted
- 15 Feb 2021
- Last updated
- 21 Feb 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT04756518
- Acronym / sponsor ID
- 20/053
- Conditions
- COVID-19, COVID, SARS-CoV 2
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 215 participants (actual)
- Lead sponsor
- East Suffolk and North Essex NHS Foundation TrustUniversity, hospital or other
- Collaborators
- University of Suffolk
- Study start
- 6 Jul 2020 (actual)
- Primary completion
- 31 Mar 2022 (actual)
- Study completion
- 31 Mar 2022 (actual)
- First posted
- 16 Feb 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| COVID 19 group | – | –The COVID 19 group will consist of peripheral blood smear slides from patients who are in the hospital who had qPCR results positive for COVID-19. |
| CONTROL group | – | –A control group will consist of i) peripheral blood smear slides from patients with no viral infection and ii) from those with a non-SARS-CoV-2 viral infection. Control group peripheral blood slides will be randomly selected from the laboratory slides archive within the facility. The laboratory slides used will be inclusive of slides archived prior to the emergence of COVID-19 infection in the United Kingdom. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Peripheral blood smears obtained from adult non-SARS-CoV-2 positive patients and from adult positive SARS-CoV-2 patients.
Key inclusion criteria (5 listed)
- 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).
Key exclusion criteria (5 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnosis of COVID-19.
Time frame: 6 months
Determine whether lymphocytes alone can diagnose COVID-19 disease with high specificity and sensitivity, using AI-based image analytical modelling.
1 secondary outcome
Severity of COVID-19 infection modelling
Time frame: 6 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Feb 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Mahesh Prahladan principal investigator
East Suffolk and North Essex NHS Foundation Trust
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID-19 and COVID, run by East Suffolk and North Essex NHS Foundation Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 21 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosis of COVID-19. (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Feb 2023.