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Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence. (NASHAI)

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ClinicalTrials.gov Identifier: NCT04099147
Recruitment Status : Not yet recruiting
First Posted : September 23, 2019
Last Update Posted : September 23, 2019
Sponsor:
Collaborator:
Servicio Cántabro de Salud
Information provided by (Responsible Party):
Instituto de Investigación Marqués de Valdecilla

Brief 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 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.

Condition or disease Intervention/treatment
Non-alcoholic Fatty Liver Disease (NAFLD) Other: This is an observational study.

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Study Type : Observational
Estimated Enrollment : 14046 participants
Observational Model: Cohort
Time Perspective: Other
Official Title: Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
Estimated Study Start Date : September 30, 2019
Estimated Primary Completion Date : September 30, 2020
Estimated Study Completion Date : December 31, 2020


Group/Cohort Intervention/treatment
ETHON
Subjects from the general population identified in the ETHON
Other: This is an observational study.
This is an observational study. No intervention is planned outside of usual clinical practice.

HEPAmet
Subjects belonging to the Spanish registry of NAFLD (HEPAmet)
Other: This is an observational study.
This is an observational study. No intervention is planned outside of usual clinical practice.




Primary Outcome Measures :
  1. 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 ]
  2. 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 ]
  3. 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 ]
  4. 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 ]
  5. 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 ]
  6. 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 ]
  7. Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis. [ Time Frame: From october of 2019 to march of 2021 ]
  8. 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 ]
  9. 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 ]


Information from the National Library of Medicine

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Ages Eligible for Study:   19 Years to 74 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
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.
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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04099147


Contacts
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Contact: Antonio Cuadrado Lavín +34942204089 antonio.cuadrado@scsalud.es
Contact: Lucía Lavín Alconero +34942204089 eclinicos5@idival.org

Sponsors and Collaborators
Instituto de Investigación Marqués de Valdecilla
Servicio Cántabro de Salud
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Responsible Party: Instituto de Investigación Marqués de Valdecilla
ClinicalTrials.gov Identifier: NCT04099147    
Other Study ID Numbers: NASHAI
First Posted: September 23, 2019    Key Record Dates
Last Update Posted: September 23, 2019
Last Verified: September 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Instituto de Investigación Marqués de Valdecilla:
non-alcoholic fatty liver disease
NAFLD
Artificial Intelligence
Deep Learning System
Additional relevant MeSH terms:
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Liver Diseases
Fatty Liver
Non-alcoholic Fatty Liver Disease
Digestive System Diseases