Predicting multidrug-resistant liver abscess using deep learning
Combining Image-clinical Model Based on Deep Learning and Radiomics to Predict Multidrug-resistant Klebsiella Pneumoniae Liver Abscess
Shengjing Hospital · NCT06506318
This study is trying to see if a new computer program can help doctors predict liver abscesses caused by tough-to-treat germs, so patients can get the right antibiotics faster.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 550 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | Shengjing Hospital (other) |
| Locations | 1 site (Shenyang, Liaoning) |
| Trial ID | NCT06506318 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop a deep learning model to predict multidrug-resistant Klebsiella pneumoniae liver abscesses by analyzing data from a multi-center database. The study focuses on the increasing prevalence of liver abscesses caused by multidrug-resistant organisms and the importance of timely antibiotic therapy. By utilizing advanced machine learning techniques, the researchers hope to improve diagnostic accuracy and treatment outcomes for patients suffering from this condition.
Who should consider this trial
Good fit: Ideal candidates include patients diagnosed with pyogenic liver abscess confirmed by surgery or interventional procedures who have undergone abdominal enhanced CT scans.
Not a fit: Patients diagnosed with other types of liver abscess, such as amoebic liver abscess, may not benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to more accurate predictions and timely treatment for patients with multidrug-resistant liver abscesses.
How similar studies have performed: Other studies utilizing deep learning for medical predictions have shown promising results, indicating potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients diagnosed as pyogenic liver abscess and was proved by surgery or interventional process. * Patients had accepted abdominal enhance CT scans before surgery or interventional process. Exclusion Criteria: * Patients diagnosed with other types of liver abscess such as amoeba.
Where this trial is running
Shenyang, Liaoning
- Shengjing hospital of China medical university — Shenyang, Liaoning, China (RECRUITING)
Study contacts
- Study coordinator: Zhihui Chang
- Email: Changzh@sj-hospital.org
- Phone: +86 18940258437
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 Abscess, Deep-learning