[R21] Deep Learning of Child Abuse Imaging: Improving Outcomes of Children Evaluated for Physical Abuse
Ente: National Library of Medicine
Scadenza: 2028-07-31
Importo max: 242.901 EUR
Paese: US
Descrizione
PROJECT SUMMARY
Fractures are a common manifestation of physical abuse, with children <2 years at highest risk. The
identification of healing fractures is crucial in the evaluation of physical abuse in a young child as these can
suggest ongoing violence within the home and have serious implications for child protection. However,
estimating time-since-injury of healing fractures based on imaging is often difficult and imprecise. Although
deep learning (DL) models could vastly improve accurate dating of healing fractures in children presenting with
suspicious injuries, a critical gap remains for accessible large digital pediatric imaging datasets and needed
artificial intelligence (AI) infrastructure. Notably, this gap has recently been designated a critical pediatric health
priority by the American College of Radiology. This project closes this gap by establishing the framework for
deidentified image sharing and storage between three PEDSnet sites (Nationwide Children’s Hospital,
Cincinnati Children’s Hospital Medical Center, Riley Hospital for Children) via a Secure File Transfer Protocol
and providing the AI infrastructure needed for better image interpretation and diagnosis. We will train and
validate DL models with state-of-the-art transformers such as DINOv3 and benchmark to the well-established
convolutional neural network architecture ResNet-50 using skeletal imaging of accidental fractures of long
bones in children <4 years to directly and accurately age healing fractures. In parallel, we will use meta-
learning with a combination of labeled accidental fractures and unlabeled abuse fractures, followed by few-shot
learning to regress the age of abuse fractures. Deliverables include establishing the framework for image
sharing within pediatric health systems and the development of DL algorithms for aging of healing fractures
that could be implemented widely as a virtual consultant for radiologists faced with the task of interpreting
imaging completed in children presenting with high-risk injuries. This is the first study to propose the
development of DL algorithms for aging healing fractures by 1) training on multicenter imaging data and 2)
using real-world data of patients evaluated for abuse. This proposal is a key first step towards development of
a national resource to stimulate and support high-quality, collaborative imaging research within pediatrics,
dramatically improving patient outcomes within both pediatric and community settings. By providing a
mechanism for cross-site image sharing, this project enables future scalable multi-institutional model
development and validation for improved interpretation of imaging completed in child abuse evaluations.
Istituzione: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
PI: Farah Wadia Brink
Progetto: 1R21LM015417-01
Settori: National Library of Medicine
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