Predicting morbidity and mortality of preterm infants by analyzing chest x-ray images at admission using deep learning algorithms
- Conditions
- Bronchopulmonary DysplasiaPrematurity
- Registration Number
- DRKS00028640
- Lead Sponsor
- niversitätsklinikum des Saarlandes
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Complete
- Sex
- All
- Target Recruitment
- 169
Inclusion Criteria
Birth weight 400-1000 g
- Oxygen supply or respiratory support within 72 h
- Basic vitamin A supplementation of 1000 IU/kg/day
- Postnatal age < 72 h
- Minimal enteral feeding
Exclusion Criteria
Congenital malformations; congenital, non-bacterial infections at the time of birth; severe peripartum asphyxia (umbilical artery pH < 6.8 > 2 hours or persistent bradycardia (heart rate < 100 beats/minute) associated with hypoxia > 2 hours. Lack of parental consent and contraindications or hypersensitivity to the drug used, Vitadral®, are also exclusion criteria.
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method sing deep learning algorithms to predict the respiratory outcome of ELBW preterm infants from a chest x-ray on admission
- Secondary Outcome Measures
Name Time Method Prediction of mortality and morbidity