Using AI to predict liver fibrosis severity
Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
Zhejiang Provincial People's Hospital · NCT06509230
This study is testing a new AI system to see if it can accurately predict the severity of liver fibrosis in people with chronic hepatitis B without needing invasive procedures.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 700 (estimated) |
| Ages | 18 Years to 60 Years |
| Sex | All |
| Sponsor | Zhejiang Provincial People's Hospital (other) |
| Locations | 1 site (Hangzhou, Zhejiang) |
| Trial ID | NCT06509230 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop a non-invasive intelligent grading diagnosis system for liver fibrosis using deep learning techniques. Patients with chronic hepatitis B will undergo liver biopsy, and their clinical, imaging, and biochemical data will be analyzed alongside data from healthy volunteers. The study employs a convolutional neural network (CNN) to extract relevant features from multi-modal data, aiming to accurately classify the severity of liver fibrosis. By leveraging a large dataset collected over four years, the study seeks to improve diagnostic accuracy and patient outcomes.
Who should consider this trial
Good fit: Ideal candidates for this study are adults aged 18-60 diagnosed with chronic hepatitis B without liver cancer.
Not a fit: Patients with contraindications for liver biopsy or liver pathology that does not meet the study criteria may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could provide a more accurate and non-invasive method for diagnosing liver fibrosis in patients.
How similar studies have performed: While the use of AI in medical diagnostics is gaining traction, this specific approach to liver fibrosis classification is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Age of 18-60 years old 2. The diagnosis of chronic hepatitis B is in line with the diagnostic criteria of China's 2019 Chronic Hepatitis B Prevention and Treatment Guidelines, and the diagnosis of non-alcoholic fatty liver is in line with the Asian Pacific Hepatology Association guidelines 3. Imaging showed no liver cancer Exclusion Criteria: 1. There are contraindications for liver biopsy 2. Liver pathology did not meet the criteria
Where this trial is running
Hangzhou, Zhejiang
- Haijun Huang — Hangzhou, Zhejiang, China (RECRUITING)
Study contacts
- Study coordinator: Haijun Huang
- Email: huanghaijun0826@163.com
- Phone: 13758186635
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Liver Fibrosis, Chronic hepatitis, Liver fibrosis, Artificial intelligence, Auxiliary diagnosis