aceRepurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study
Despite growing clinical adoption of artificial intelligence in radiology, fewer than one in five commercially available models are cleared for paediatric use. Existing systems are trained predominantly on adult data and generalize poorly to neonates, yet efficient strategies for repurposing them without data sharing or large-scale local data curation are lacking. Here we present an international retrospective study of neonatal chest radiographs from 30 neonatal intensive care units across 20 countries and terri…
Retraining an adult endotracheal tube model sequentially on 50 radiographs per hospital cut neonatal landmark localization error well below fine-tuning or adult-only use, without centralizing patient images.
The study is retrospective and reports radiograph-level localization and detection performance, so it does not show clinical outcomes or prospective use in neonatal care.
Evidence
- Peer-reviewedNature Biomedical Engineering2026-10-13
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Truvace Impact Record TRV-2026-1366, v1: “Repurposing adult AI radiograph models for neonatal care through continual learning: an international multicentre study.” Truvace, 2026-10-14. /record/TRV-2026-1366 (accessed at citation time). sha256 95f6ce429422aa96…
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