Liver SteatosesST-AINCT06103175
Observational and Prospective Study of Hepatic Steatosis and Related Risk Factors Using Ultrasound and Artificial Intelligence
An observational study of Liver Steatoses, run by University of Bari at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Liver Steatoses
- How many people
- 150 participants
- Where
- 1 siteItaly
- Study dates
- 15 Jan 2023to 1 Nov 2024
- 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 18 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- Age between 18-70 years
- MRI regardless of clinical indications,
- written informed consent
Who cannot join
- cirrhosis
- hepatocellular carcinoma or any liver tumours,
- absence of the right kidney
- previous liver transplantation
- large liver cysts or kidney cysts
Trial locations and contacts
1 site in 1 country.
Italy1 site
Department of Department of Precision and Regenerative Medicine and Ionian Area (DiMePre-J - Clinica medica "A. Murri"
Bari, BA, 70124
About this study
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
Read the detailed description
In recent years, ultrasound has taken on a predominant role in the evaluation of liver steatosis, as it is a non-invasive, non-irradiating method that is easily reproducible and inexpensive. Of particular effectiveness is the use of the hepatorenal index, evaluated as the intensity ratio (echogenicity) between the hepatic parenchyma and the renal cortical parenchyma. The main limitations of detecting the hepato-renal index during abdominal ultrasound, however, are operator dependence and the use of a relatively long time span to complete the sequence of operations and calculations required to determine the index itself. The use of Artificial Intelligence (AI) techniques for image analysis in the medical field is yielding excellent results. AI-based algorithms are increasingly a powerful tool that allows the physician to improve their performance in terms of speed and accuracy of clinical evaluations. Today, there is already evidence of the effectiveness of using AI on ultrasound images for clinical evaluations. The use of AI as an aid in diagnosing liver diseases through ultrasound is still under-researched. The hypothesis to be tested is the utility that AI can have in the evaluation, its general and specific uses in reducing calculation times of the hepatorenal index.
In this study, 134 patients were enrolled with no clinical suspicion of liver steatosis. All patients underwent abdominal ultrasonography (US) and magnetic resonance imaging fat fraction (MRI-PDFF), assumed as reference technique to evaluate the grade of steatosis. The hepatorenal index (US) was manually calculated (HRIM) by 4 skilled operators. An automatic hepatorenal index calculation (HRIA) was also obtained by an algorithm. The accuracy of HRIA to discriminate different grades of fatty liver was evaluated by Receiver operating characteristic (ROC) analysis using MRI-PDFF cut-offs.
- Conditions
- Liver Steatoses
- Medical subject headings
- Fatty Liver
What is being measured
The primary outcome is the main question the study is designed to answer.
Hepato-renal index calculation
Calculation of the Hepatorenal Index manually and automatically using the AI-based algorithm.
Measured: 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
Measured: 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Oct 2023
- Study start15 Jan 2023
- Primary completion15 Jun 2023
- Study completion1 Nov 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver SteatosesStudies 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
- Cross-sectional
- Enrolment
- 150 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Bari
- Sponsor type
- University, hospital or other
- Collaborators
- Eurisko Technology srl, Centro Radiologico Lucano
- Responsible party
- Principal investigator, piero portincasa, University of Bari
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06103175
- Sponsor's protocol ID
- AI-steatosis
- Acronym
- ST-AI
- Status last verified
- Nov 2023
- First submitted
- 21 Oct 2023
- Last updated
- 9 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Observational and Prospective Study of Hepatic Steatosis and Related Risk Factors Using Ultrasound and Artificial Intelligence
- ClinicalTrials.gov ID
- NCT06103175
- Acronym / sponsor ID
- ST-AI / AI-steatosis
- Conditions
- Liver Steatoses
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 150 participants (actual)
- Lead sponsor
- University of BariUniversity, hospital or other
- Collaborators
- Eurisko Technology srl, Centro Radiologico Lucano
- Study start
- 15 Jan 2023 (actual)
- Primary completion
- 15 Jun 2023 (actual)
- Study completion
- 1 Nov 2024 (estimated)
- First posted
- 26 Oct 2023 (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.
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
Detailed description
In recent years, ultrasound has taken on a predominant role in the evaluation of liver steatosis, as it is a non-invasive, non-irradiating method that is easily reproducible and inexpensive. Of particular effectiveness is the use of the hepatorenal index, evaluated as the intensity ratio (echogenicity) between the hepatic parenchyma and the renal cortical parenchyma. The main limitations of detecting the hepato-renal index during abdominal ultrasound, however, are operator dependence and the use of a relatively long time span to complete the sequence of operations and calculations required to determine the index itself. The use of Artificial Intelligence (AI) techniques for image analysis in the medical field is yielding excellent results. AI-based algorithms are increasingly a powerful tool that allows the physician to improve their performance in terms of speed and accuracy of clinical evaluations. Today, there is already evidence of the effectiveness of using AI on ultrasound images for clinical evaluations. The use of AI as an aid in diagnosing liver diseases through ultrasound is still under-researched. The hypothesis to be tested is the utility that AI can have in the evaluation, its general and specific uses in reducing calculation times of the hepatorenal index.
In this study, 134 patients were enrolled with no clinical suspicion of liver steatosis. All patients underwent abdominal ultrasonography (US) and magnetic resonance imaging fat fraction (MRI-PDFF), assumed as reference technique to evaluate the grade of steatosis. The hepatorenal index (US) was manually calculated (HRIM) by 4 skilled operators. An automatic hepatorenal index calculation (HRIA) was also obtained by an algorithm. The accuracy of HRIA to discriminate different grades of fatty liver was evaluated by Receiver operating characteristic (ROC) analysis using MRI-PDFF cut-offs.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Age between 18-70 years
- MRI regardless of clinical indications,
- written informed consent
Key exclusion criteria (5 listed)
- cirrhosis
- hepatocellular carcinoma or any liver tumours,
- absence of the right kidney
- previous liver transplantation
- large liver cysts or kidney cysts
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Hepato-renal index calculation
Time frame: 4 months
Calculation of the Hepatorenal Index manually and automatically using the AI-based algorithm.
Magnetic Resonance scanning and fat percentage evaluation
Time frame: 4 months
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2023.
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Liver Steatoses, run by University of Bari at 1 site in Italy.
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 3 inclusion and 5 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 9 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Hepato-renal index calculation (time frame: 4 months). The study lists 2 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 9 Nov 2023.