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

NCT04099147 | Unknown

• 1 other identifier: NASHAI
• Study Type: observational
• Target: 14,046
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Non-alcoholic Fatty Liver Disease (NAFLD)

Keywords

non-alcoholic fatty liver disease; NAFLD; Artificial Intelligence; Deep Learning System

Outcome Measures

Primary Outcomes (9)

• Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms

  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

  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

  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

  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.

  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.

  From october of 2019 to march of 2021

• Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis.

  From october of 2019 to march of 2021

• Kappa coefficient of concordance about NASH diagnosis between AI algorithms and the Hepamet non-invasive score.

  From october of 2019 to march of 2021

• ROC curve at various threshold settings obtained through the algorithms for NASH diagnosis and staging

  From october of 2019 to march of 2021

Study Arms (2)

ETHON

Subjects from the general population identified in the ETHON

Other: This is an observational study.

HEPAmet

Subjects belonging to the Spanish registry of NAFLD (HEPAmet)

Other: This is an observational study.

Interventions

This is an observational study. OTHER

This is an observational study. No intervention is planned outside of usual clinical practice.

ETHON; HEPAmet

Eligibility Criteria

• Age: 19 Years - 74 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• Subjects aged 19-74 belonging to the ETHON cohort or registered in the Hepamet Spanish registry of NAFLD or the European NAFLD registry

Sponsors & Collaborators

• Instituto de Investigación Marqués de Valdecilla: lead
• Servicio Cántabro de Salud: collaborator

MeSH Terms

Conditions

Non-alcoholic Fatty Liver Disease

Condition Hierarchy (Ancestors)

Fatty Liver; Liver Diseases; Digestive System Diseases

Central Study Contacts

Antonio Cuadrado Lavín

CONTACT

+34942204089; antonio.cuadrado@scsalud.es

Lucía Lavín Alconero

CONTACT

+34942204089; eclinicos5@idival.org

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 20, 2019
• First Posted: September 23, 2019
• Study Start: September 30, 2019
• Primary Completion: September 30, 2020
• Study Completion: December 31, 2020
• Last Updated: September 23, 2019

Record last verified: 2019-09

Data Sharing

• IPD Sharing: Will not share