Clinical trial NCT04099147: Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
Official title: Diagnosis and Characterization of Non-Alcoholic Fatty Liver Disease Based on Artificial Intelligence.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Non-alcoholic Fatty Liver Disease (NAFLD)
Keywords: non-alcoholic fatty liver disease
NAFLD
Artificial Intelligence
Deep Learning System
Lead sponsor: Instituto de Investigación Marqués de Valdecilla
Sponsor class: OTHER
Collaborators: name: Servicio Cántabro de Salud
First posted: 2019-09-23
Enrollment: 14046
Sex: ALL
Minimum age (years): 19
Maximum age (years): 74
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
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.

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.

Detailed description: The objectives of this observational study are the following:

1. To design a predictive model of significant liver disease due to NAFLD, based on clustering or clustering algorithms (AI)
2. 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
3. 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:

1. - Study cohort (Phases I-III):

   A. 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.
2. - 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.

Eligibility 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.

Primary outcomes:
measure: Number of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
timeFrame: From october of 2019 to march of 2021
measure: Percentage of subjects diagnosed with NAFLD and NASH in the ETHON cohort after applying Artificial Intelligence algorithms
timeFrame: From october of 2019 to march of 2021
measure: Sensitivity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
timeFrame: From october of 2019 to march of 2021
measure: Specificity in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score
timeFrame: From october of 2019 to march of 2021
measure: Positive predictive value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
timeFrame: From october of 2019 to march of 2021
measure: Negative predictive Value in terms of NASH diagnosis of AI algorithms with respect to histologic diagnosis compared with the Hepamet non-invasive score.
timeFrame: From october of 2019 to march of 2021
measure: Kappa coefficient of concordance about NASH diagnosis between AI algorithms and histologic diagnosis.
timeFrame: From october of 2019 to march of 2021
measure: Kappa coefficient of concordance about NASH diagnosis between AI algorithms and the Hepamet non-invasive score.
timeFrame: From october of 2019 to march of 2021
measure: ROC curve at various threshold settings obtained through the algorithms for NASH diagnosis and staging
timeFrame: From october of 2019 to march of 2021
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
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