Non-alcoholic Fatty Liver Disease (NAFLD)NASHAINCT04099147
Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
An observational study of Non-alcoholic Fatty Liver Disease (NAFLD), run by Instituto de Investigación Marqués de Valdecilla.
Trial summary
A key element in the diagnosis of non-alcoholic fatty liver disease (NAFLD) is the differentiation of non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver (NAFL) and the staging of the liver fibrosis, given that patients with NASH and advanced fibrosis are those at greatest risk of developing hepatic… Read more about this study
- Who is it for?
- Ages 19 to 74 years
- Conditions the study is for
- Non-alcoholic Fatty Liver Disease (NAFLD)
- What is being tested?
- This is an observational study.
- How many people
- 14,046 participants
- Where
- No sites listed yet
- Study dates
- 30 Sep 2019to 31 Dec 2020
- 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 19 to 74 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study has four phases: Phases I and II refer to both unsupervised and supervised artificial intelligence learning to identify clusters and build diagnostic algorithms. They will be carried out on data generated from the ETHON cohort. Phase III will consist on applying deep learning system technology as a support strategy to stratify liver biopsies in NALFD patients according to their grade of necro-inflammation and stage of fibrosis. Liver biopsies collected in the Spanish registry of NAFLD up to the beginning of the study will be used. Finally, a phase IV of validation will be performed with data from patients that are going to be registered in the European and Spanish registries of NAFLD.
Who may be able to join
- Subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry
Who cannot join
- Subjects that not fulfill the inclusion criteria and those who did not sign informed consent to participate in the ETHON cohort or to be registered in the mentioned registers.
Trial locations and contacts
No sites are listed yet.
About this study
A key element in the diagnosis of non-alcoholic fatty liver disease (NAFLD) is the differentiation of non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver (NAFL) and the staging of the liver fibrosis, given that patients with NASH and advanced fibrosis are those at greatest risk of developing hepatic complications and cardiovascular disease. There are still no available non-invasive methods that allow for correct diagnosis and staging of NAFLD. The implementation of Artificial Intelligence (AI) techniques based on artificial neural networks and deep learning systems (Deep Learning System) as a tool for medical diagnoses represents a bona fide technological revolution that introduces an innovative approach to improving health processes.
Read the detailed description
The objectives of this observational study are the following:
- To design a predictive model of significant liver disease due to NAFLD, based on clustering or clustering algorithms (AI)
- To apply and validate this model to classify patients according to the severity of the disease in such a manner as to provide more effective management of these patients from Primary Care to Hospital Care through process and resource optimization
- To develop a Deep Learning System based on convolutional neuronal networks for automatic recognition of images in a cohort of subjects with digitized liver biopsies, and to undertake pairwise analysis that allows for correct and exact classification of biopsies from subjects with NASH.
Design:
An observational study of the determination and validation of diagnostic predictive models of NAFLD.
The study has four phases:
Phases I and II refer to both unsupervised and supervised artificial intelligence learning to identify clusters and build diagnostic algorithms. They will be carried out on data generated from the ETHON cohort (see below).
Phase III will consist on applying deep learning system technology as a support strategy to stratify liver biopsies in NALFD patients according to their grade of necro-inflammation and stage of fibrosis. Liver biopsies collected in the Spanish registry of NAFLD up to the beginning of the study will be used.
Finally, a phase IV of validation will be performed with data from patients that are going to be registered in the Spanish registry of NAFLD.
Population:
- - Study cohort (Phases I-III):
- Subjects from the general population identified in the ETHON (Epidemiological Study of Hepatic Infections) cohort* that has already been created (12,246 subjects between 19-74 years of age) and B. Subjects belonging to the Spanish registry of NAFLD (HEPAmet) (1,800 subjects already collected at the beginning of the study) *The ETHON cohort was recruited between 2015 and 2017 to study the hepatitis C prevalence in the Spanish general population aged 19-74 years old. Lavin AC, Llerena S, Gomez M, Escudero MD, Rodriguez L, Estebanez LA, Gamez B, Puchades L, Cabezas J, Serra MA, Calleja JL, Crespo J. Prevalence of hepatitis C in the spanish population. The PREVHEP study (ETHON cohort). J Hepatol. 2017;66:S272.
- - Validation cohort (Phase IV):
Patients diagnosed with NAFLD by hepatic biopsy recruited in the Spanish and European registers from the beginning of the study.
-Inclusion and exclusion criteria:
Inclusion criteria: subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry
Exclusion criteria: subjects that not fulfill the inclusion criteria and those who did not sign informed consent to participate in the ETHON cohort or to be registered in the mentioned registers.
- Being tested
- This is an observational study.
- Keywords
- non-alcoholic fatty liver disease, NAFLD, Artificial Intelligence, Deep Learning System
- Medical subject headings
- Non-alcoholic Fatty Liver Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
This is an observational study.Other
This is an observational study. No intervention is planned outside of usual clinical practice.
| Study group | Role | What they receive |
|---|---|---|
| ETHON | Subjects from the general population identified in the ETHON Receives: Other: This is an observational study. | |
| HEPAmet | Subjects belonging to the Spanish registry of NAFLD (HEPAmet) Receives: Other: This is an observational study. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
Measured: From october of 2019 to march of 2021
Percentage of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
Measured: From october of 2019 to march of 2021
Sensitivity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
Measured: From october of 2019 to march of 2021
Specificity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
Measured: From october of 2019 to march of 2021
Positive predictive value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
Measured: From october of 2019 to march of 2021
Negative predictive Value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
Measured: From october of 2019 to march of 2021
Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis.
Measured: From october of 2019 to march of 2021
Kappa coefficient of concordance about NASH diagnosis between AI algorithms and the Hepamet non-invasive score.
Measured: From october of 2019 to march of 2021
ROC curve at various threshold settings obtained through the algorithms for NASH diagnosis and staging
Measured: From october of 2019 to march of 2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Sep 2019
- Study start30 Sep 2019 (estimated)
- Primary completion30 Sep 2020 (estimated)
- Study completion31 Dec 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Non-alcoholic Fatty Liver Disease (NAFLD)Studies 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
- Other
- Enrolment
- 14,046 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Instituto de Investigación Marqués de Valdecilla
- Sponsor type
- University, hospital or other
- Collaborators
- Servicio Cántabro de Salud
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04099147
- Sponsor's protocol ID
- NASHAI
- Acronym
- NASHAI
- Status last verified
- Sep 2019
- First submitted
- 20 Sep 2019
- Last updated
- 23 Sep 2019
- Results posted
- No
- Data monitoring committee
- No
- 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 23 Sep 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
- ClinicalTrials.gov ID
- NCT04099147
- Acronym / sponsor ID
- NASHAI / NASHAI
- Keywords
- non-alcoholic fatty liver disease, NAFLD, Artificial Intelligence, Deep Learning System
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 14,046 participants (estimated)
- Lead sponsor
- Instituto de Investigación Marqués de ValdecillaUniversity, hospital or other
- Collaborators
- Servicio Cántabro de Salud
- Study start
- 30 Sep 2019 (estimated)
- Primary completion
- 30 Sep 2020 (estimated)
- Study completion
- 31 Dec 2020 (estimated)
- First posted
- 23 Sep 2019 (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.
A key element in the diagnosis of non-alcoholic fatty liver disease (NAFLD) is the differentiation of non-alcoholic steatohepatitis (NASH) from non-alcoholic fatty liver (NAFL) and the staging of the liver fibrosis, given that patients with NASH and advanced fibrosis are those at greatest risk of developing hepatic complications and cardiovascular disease. There are still no available non-invasive methods that allow for correct diagnosis and staging of NAFLD. The implementation of Artificial Intelligence (AI) techniques based on artificial neural networks and deep learning systems (Deep Learning System) as a tool for medical diagnoses represents a bona fide technological revolution that introduces an innovative approach to improving health processes.
Detailed description
The objectives of this observational study are the following:
- To design a predictive model of significant liver disease due to NAFLD, based on clustering or clustering algorithms (AI)
- To apply and validate this model to classify patients according to the severity of the disease in such a manner as to provide more effective management of these patients from Primary Care to Hospital Care through process and resource optimization
- To develop a Deep Learning System based on convolutional neuronal networks for automatic recognition of images in a cohort of subjects with digitized liver biopsies, and to undertake pairwise analysis that allows for correct and exact classification of biopsies from subjects with NASH.
Design:
An observational study of the determination and validation of diagnostic predictive models of NAFLD.
The study has four phases:
Phases I and II refer to both unsupervised and supervised artificial intelligence learning to identify clusters and build diagnostic algorithms. They will be carried out on data generated from the ETHON cohort (see below).
Phase III will consist on applying deep learning system technology as a support strategy to stratify liver biopsies in NALFD patients according to their grade of necro-inflammation and stage of fibrosis. Liver biopsies collected in the Spanish registry of NAFLD up to the beginning of the study will be used.
Finally, a phase IV of validation will be performed with data from patients that are going to be registered in the Spanish registry of NAFLD.
Population:
- - Study cohort (Phases I-III):
- Subjects from the general population identified in the ETHON (Epidemiological Study of Hepatic Infections) cohort* that has already been created (12,246 subjects between 19-74 years of age) and B. Subjects belonging to the Spanish registry of NAFLD (HEPAmet) (1,800 subjects already collected at the beginning of the study) *The ETHON cohort was recruited between 2015 and 2017 to study the hepatitis C prevalence in the Spanish general population aged 19-74 years old. Lavin AC, Llerena S, Gomez M, Escudero MD, Rodriguez L, Estebanez LA, Gamez B, Puchades L, Cabezas J, Serra MA, Calleja JL, Crespo J. Prevalence of hepatitis C in the spanish population. The PREVHEP study (ETHON cohort). J Hepatol. 2017;66:S272.
- - Validation cohort (Phase IV):
Patients diagnosed with NAFLD by hepatic biopsy recruited in the Spanish and European registers from the beginning of the study.
-Inclusion and exclusion criteria:
Inclusion criteria: subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry
Exclusion criteria: subjects that not fulfill the inclusion criteria and those who did not sign informed consent to participate in the ETHON cohort or to be registered in the mentioned registers.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| ETHON | – | Other: This is an observational study.Subjects from the general population identified in the ETHON |
| HEPAmet | – | Other: This is an observational study.Subjects belonging to the Spanish registry of NAFLD (HEPAmet) |
Who may qualify
- Ages
- 19 to 74 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study has four phases: Phases I and II refer to both unsupervised and supervised artificial intelligence learning to identify clusters and build diagnostic algorithms. They will be carried out on data generated from the ETHON cohort. Phase III will consist on applying deep learning system technology as a support strategy to stratify liver biopsies in NALFD patients according to their grade of necro-inflammation and stage of fibrosis. Liver biopsies collected in the Spanish registry of NAFLD up to the beginning of the study will be used. Finally, a phase IV of validation will be performed with data from patients that are going to be registered in the European and Spanish registries of NAFLD.
Key inclusion criteria (1 listed)
- Subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry
Key exclusion criteria (1 listed)
- Subjects that not fulfill the inclusion criteria and those who did not sign informed consent to participate in the ETHON cohort or to be registered in the mentioned registers.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| This is an observational study.This is an observational study. No intervention is planned outside of usual clinical practice. | Other | ETHON, HEPAmet |
Primary outcome
Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
Time frame: From october of 2019 to march of 2021
Percentage of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
Time frame: From october of 2019 to march of 2021
Sensitivity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
Time frame: From october of 2019 to march of 2021
Specificity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
Time frame: From october of 2019 to march of 2021
Positive predictive value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
Time frame: From october of 2019 to march of 2021
Negative predictive Value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
Time frame: From october of 2019 to march of 2021
Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis.
Time frame: From october of 2019 to march of 2021
Kappa coefficient of concordance about NASH diagnosis between AI algorithms and the Hepamet non-invasive score.
Time frame: From october of 2019 to march of 2021
ROC curve at various threshold settings obtained through the algorithms for NASH diagnosis and staging
Time frame: From october of 2019 to march of 2021
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Sep 2019.
- 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.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-alcoholic Fatty Liver Disease (NAFLD), run by Instituto de Investigación Marqués de Valdecilla.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 23 Sep 2019. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Antonio Cuadrado Lavín (+34942204089, antonio.cuadrado@scsalud.es), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms (time frame: From october of 2019 to march of 2021). The study lists 9 primary outcomes in all. 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 23 Sep 2019.