AI Enabled App for Fracture Reduction Prediction in Pediatrics

Document text

Principal Investigator: Kevin R. Cleary
Organization: MIRA MEDICAL, LLC
Fiscal Year: 2024
Award: $306,858
Funding agency: National Institute of Arthritis and Musculoskeletal and Skin Diseases

Project Summary
The incidence of pediatric fractures is reported to be in the range of 12 to 36.1 per 1000 per year, with forearm
fractures constituting approximately 40% of all long bone fractures. A timely and accurate diagnosis of forearm
fractures is crucial to restore function and prevent complications such as persistent pain, stiffness, or growth
plate arrest. The primary diagnostic approach involves physical examination and radiography. The treatment
goal is to restore length and alignment between the distal and proximal bone fragments. While minimally
displaced fractures may necessitate only immobilization for comfort through splinting or casting, moderately or
severely displaced fractures often require reduction for realignment. Parents often take their children with
suspected fractures to adult-based or urgent care medical centers, which lack the resources required for
specialized pediatric care, leading to transfers to pediatric tertiary care centers and/or urgent consultations from
pediatric orthopedic surgeons with specialized training in pediatric orthopedic injuries. To address and mitigate
this healthcare burden, we propose the development of a machine learning framework named the Forearm
Fracture AI-driven Recommendation System (FFAIRS) to improve forearm fracture management in
pediatrics. Our primary goal is to leverage machine learning to generate recommendations for treating forearm
fractures based on clinical presentation and x-ray analysis.
Aim 1: Develop a machine learning framework for generating treatment recommendations for pediatric
forearm fractures.
Aim 2: Retrospectively evaluate FFAIRS for accurate prediction and improved patient outcomes.

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