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Predicting morbidity and mortality of preterm infants by analyzing chest x-ray images at admission using deep learning algorithms

Predicting morbidity and mortality of preterm infants by analyzing chest x-ray images at admission using deep learning algorithms

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00028640
Enrollment
169
Registered
2022-05-03
Start date
2022-05-03
Completion date
Unknown
Last updated
2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Bronchopulmonary Dysplasia Prematurity

Interventions

Group 1: Analysis of chest x-ray

Sponsors

Universitätsklinikum des Saarlandes
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 3 Days

Inclusion criteria

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

Exclusion criteria: Congenital malformations; congenital, non-bacterial infections at the time of birth; severe peripartum asphyxia (umbilical artery pH 2 hours or persistent bradycardia (heart rate 2 hours. Lack of parental consent and contraindications or hypersensitivity to the drug used, Vitadral®, are also exclusion criteria.

Design outcomes

Primary

MeasureTime frame
Using deep learning algorithms to predict the respiratory outcome of ELBW preterm infants from a chest x-ray on admission—

Secondary

MeasureTime frame
Prediction of mortality and morbidity—

Countries

Germany

Contacts

Public ContactMichael Zemlin

Universitätsklinikum des Saarlandes, Klinik für Allgemeine Pädiatrie und Neonatologie

neonatologie@uks.eu+4968411628412

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026