Research4Babies/CSAR/RETAIN

Medical AI is built for adults, and newborns are left behind. Out today in Nature Biomedical Engineering (@natBME): we took an AI model built to check breathing tube placement on adult X-rays and adapted it for newborns by sending it to 30 NICUs in 20 countries and territories. Learning from just 50 X-rays at each, it pinpointed the tube tip and the base of the windpipe more accurately than hospitals' own models at 29 of 30 sites, even at hospitals it had never trained on. Why it matters: only 17% of FDA-authorized AI medical devices are labeled for use in children, largely because pediatric data is scarce and hard to share. Out of the box, the adult model misplaced the tube tip on newborn X-rays by more than 5 cm, about the length of a newborn's windpipe, in 37% of cases. So instead of pooling data, the model travels: it's fine-tuned on 50 X-rays at one hospital, then passed to the next. We simulated this using real X-rays from 30 NICUs, an approach designed so patient data never has to leave a hospital. Across 30 NICUs: • Large errors (over 5 cm) fell from 37% to 2.6% • Better at flagging dangerously deep breathing tubes at 27 of 30 sites • Gains grew with every hospital added, with no plateau It's been surreal working with such a diverse global team. My goal is closing the gap between AI development and real impact for patients. This is one step: it removes a major feasibility barrier, scarce and siloed pediatric data, and could help open the door to a new generation of pediatric AI. Paper, code and figures below 👇 Grateful to @pranavrajpurkar, @emma_ychen and our team of 75 researchers across 20 countries and territories (full team below) @harvardmed @PennMedicine @BrighamWomens @StanfordMed @GreatOrmondSt @KU_Leuven @UAlberta @UniklinikEssen @TelAvivUni @OsakaMetUniv_en @AUB_Lebanon @AUBMC_Official @SidraMedicine @ChildrensColo @SLU_Official @Newark_Beth @MedUnivSC