Observational and Prospective Study of Hepatic Steatosis and Related Risk Factors Using Ultrasound and Artificial Intelligence

NCT06103175 | Unknown

• 1 other identifier: AI-steatosis
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

Fatty liver is the most frequent chronic liver disease worldwide and ultrasonography is widely employed for diagnosis. The accuracy of this technique, however, is strongly operator-dependent. Few information is available, so far, on the possible use of algorithms based on Artificial Intelligence (AI) to ameliorate the diagnostic accuracy of ultrasonography in diagnosing fatty liver. This study showed that the use of AI is able to improve the diagnostic accuracy of ultrasonography in the diagnosis of fatty liver

Conditions

Liver Steatoses

Outcome Measures

Primary Outcomes (2)

• Hepato-renal index calculation

  Calculation of the Hepatorenal Index manually and automatically using the AI-based algorithm.

  4 months

• Magnetic Resonance scanning and fat percentage evaluation

  Proton Density Fat Fraction MRI scans (MRI-PDFF) to evaluate the liver fat percentage as the average value of percentage of fat evaluated for each liver segment

  4 months

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Participants were consecutively recruited on a voluntary basis between January 2023 and April 2023 among those entering a radiologic center (Centro Radiologico Lucano, Matera, Italy) for a previously prescribed abdominal magnetic resonance imaging (MRI) study. At enrolment, all subjects underwent a clinical examination consisting of detailed history and physical examination to rule out any organic or functional disease potentially interfering with the study.

You may qualify if:

• Age between 18-70 years
• MRI regardless of clinical indications,
• written informed consent

You may not qualify if:

• cirrhosis
• hepatocellular carcinoma or any liver tumours,
• absence of the right kidney
• previous liver transplantation
• large liver cysts or kidney cysts

Sponsors & Collaborators

• University of Bari: lead
• Eurisko Technology srl: collaborator
• Centro Radiologico Lucano: collaborator

Study Sites (1)

Department of Department of Precision and Regenerative Medicine and Ionian Area (DiMePre-J - Clinica medica "A. Murri"

Bari, BA, 70124, Italy

Location

MeSH Terms

Conditions

Fatty Liver

Condition Hierarchy (Ancestors)

Liver Diseases; Digestive System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, MD

Study Record Dates

• First Submitted: October 21, 2023
• First Posted: October 26, 2023
• Study Start: January 15, 2023
• Primary Completion: June 15, 2023
• Study Completion: November 1, 2024
• Last Updated: November 9, 2023

Record last verified: 2023-11

Locations

IT (1)