ArticleNPJ digital medicine2025
NeoCLIP: a self-supervised foundation model for the interpretation of neonatal radiographs.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs.Pediatric radiology · 2026Article
- FDA-Regulated AI-Enabled Medical Devices With Pediatric Indications.JAMA network open · 2026Article
- Large Language Models Using Clinical Text in Pediatrics: A Scoping Review.JAMA network open · 2026Article
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep learning has proven to be an excellent tool for interpreting medical images in adults and children. However, it is still underdeveloped for neonates. This study developed NeoCLIP, a novel deep contrastive learning model designed to detect pathologies and medical devices on neonatal radiographs. We retrospectively studied 4629 infants admitted to a neonatal intensive care unit in Boston, MA (2008-2023), compiling 20,154 radiographs and 15,795 corresponding reports. The cohort was randomized into training (80%), validation (10%), and test (10%) sets. NeoCLIP was trained to identify 15 radiological features and 5 medical devices relevant to neonatal intensive care. NeoCLIP achieved higher AUROC compared to controls in all labels except portal venous gas. Incorporating demographic data improved model performance, though not statistically significantly. NeoCLIP is the first deep learning model tailored for the interpretation of neonatal radiographs, surpassing the efficacy of comparable adult models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.