The Role of Artificial Intelligence in Predicting and Managing Pediatric Fracture Overgrowth: A Comprehensive Review
Source abstract
Abstract
Pediatric fracture overgrowth is an unpredictable complication of long bone fractures in children, leading to excessive growth of the injured limb and resulting in limb length discrepancies (LLDs) and angular deformities that impact mobility and quality of life. Traditional methods struggle to predict at-risk children, hindering early detection and management. Artificial intelligence (AI), including machine learning and deep learning, offers advanced data analysis capabilities to enhance predictive accuracy and personalize treatment strategies. This comprehensive review explores the current understanding of pediatric fracture overgrowth, examines AI applications in medicine and orthopedics, evaluates potential AI applications specific to fracture overgrowth, and discusses ethical considerations and patient-centric approaches. We highlight how AI can improve diagnostic precision, facilitate early intervention, and optimize clinical outcomes. Though direct studies on AI in fracture overgrowth are limited, evidence from related areas underscores its potential. Embracing AI could revolutionize pediatric fracture management, leading to earlier detection, targeted interventions, and better outcomes for affected children.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare and Education · Autopsy Techniques and Outcomes · Trauma and Emergency Care Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Section of Orthopaedics and Traumatology, Department of Biomedical Sciences and Morphological and Functional Images, University of Messina, 98122 Messina, Italy
Department of Orthopaedic and Trauma Surgery, Università Campus Bio-Medico Di Roma, 00128 Rome, Italy
Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy
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