Completed

NCT Number: NCT07721454

AI-ACLD Study: Artificial Intelligence in Advanced Chronic Liver Disease

The research project studies the possibility of using an artificial intelligence-based system in patients with advanced chronic liver disease (liver cirrhosis) to record variations in a patient's health status, with the aim of early identification of clinical improvement or deterioration. The system is based on the collection and processing of various clinical parameters through an Apple Watch. The study aims to evaluate whether the data generated by this system correlate with patients' clinical evolution and whether its use may ultimately contribute to improved care management and quality of life.

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Key information

Conditions

Advance Chronic Liver Disease (ACLD)

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Ente Ospedaliero Cantonale

Lugano, 6900, Switzerland

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Adults aged 18-75 years
Diagnosis of advanced chronic liver disease (ACLD).
Either:
hospitalized for hepatic decompensation or acute-on-chronic liver failure (ACLF), including ascites, hepatorenal syndrome, hepatic encephalopathy, bacterial infection, gastrointestinal bleeding, or jaundice; or
outpatient with Child-Pugh B cirrhosis and no evidence of hepatic decompensation or ACLF at enrolment.
Willing and able to provide written informed consent.

Exclusion criteria

Inability or refusal to provide written informed consent
Inability to wear or correctly use the Apple Watch
Patients with hepatocellular carcinoma beyond the Milan Criteria (one lesion up to 5 cm or 3 lesions up to 3 cm in diameter)
Presence of hepatic decompensation or ACLF

Treatment and study plan

Apple Watch

Device

Apple Watch has been used to specifically monitor patients with liver cirrhosis, who worn it for the period specified by the study

Primary outcomes

Feasibility and accuracy of machine learning analysis of individual health data collected by wearable device.

Time frame: From enrollment to the end of the study (6 months)

The primary outcome is to assess the feasibility and accuracy of machine learning analysis of individual health data collected by wearable device and to describe their patterns during hospitalization due to symptoms of decompensation or ACLF and in outpatients until hospitalization due to decompensation or ACLF in patients with liver cirrhosis. Health data continuously collected through a dedicated wearable device application comprise: heart rate and heart rate variability (HR; HRV), oxygen saturation (SpO₂), ECG (QRS, PQ, PT Tpe interval), sleep quality and duration, daily step count, tremor intensity (Hz), typing speed (taps/time).

The single unit of measure used to assess the feasibility of the wearable device is the usable data acquisition rate (%), defined as the percentage of monitoring data successfully collected and suitable for analysis.

Sponsors and collaborators

Lead sponsor

Antonio Galante

Other

Registry information

Official study title

Application of Artificial Intelligence Using Wearable Technology in Patients With Advanced Chronic Liver Disease (ACLD): a Trajectomics Approach.

Acronym: AI-ACLD

Important dates

Study start: 2022
Primary completion: 2024
Study completion: 2024
First posted: Jul 23, 2026
Registry last updated: Jul 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.