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Automated Analysis of Pelvic Radiographs for Hip Dysplasia Screening Using Artificial Intelligence in Children with
Ayesha Barmare1, Erich Rutz1,2,3,4,5,6, Sharmala Thuraisingam1,4
1Department of Paediatrics, The Royal Children's Hospital, The University of Melbourne, Melbourne 3052, Australia.
Insights
Artificial intelligence shows promise in detecting hip dysplasia in children with cerebral palsy. AI models achieved high sensitivity and specificity, potentially improving early intervention and hip surveillance.
Area of Science:
- Pediatric Orthopedics
- Medical Imaging
- Artificial Intelligence
Background:
- Cerebral palsy affects millions globally, often leading to hip dysplasia in children.
- Hip dysplasia can cause pain and functional decline, necessitating regular surveillance.
- Current manual screening methods are time-consuming and costly.
Purpose of the Study:
- To compare the performance of artificial intelligence (AI) models against expert clinicians in detecting hip dysplasia.
- To evaluate AI's potential as an adjunct tool for hip surveillance in pediatric cerebral palsy patients.
Main Methods:
- Systematic literature search of Embase, Ovid MEDLINE, and Web of Science up to July 2025.
- Inclusion of studies on AI detection of hip dysplasia in children (≤18 years) with cerebral palsy.
- Risk of bias assessment using QUADAS-2 and narrative synthesis.
Main Results:
- Six studies involving over 4000 radiographs were analyzed.
- AI sensitivity ranged from 70% to 97.4%; specificity ranged from 85% to 96%.
- Area under the curve values were high (0.923–0.999), though most studies had moderate to high risk of bias.
Conclusions:
- AI demonstrates significant potential as a supplementary tool for hip surveillance in children with cerebral palsy.
- AI may offer a more efficient and accurate method for early detection of hip dysplasia.
- Further validation, particularly external validation, is needed to confirm AI's clinical utility.
Abstract:
Background and Objectives: Cerebral palsy is a debilitating and complex movement disorder affecting millions of people worldwide. Many children with cerebral palsy develop hip dysplasia, which can lead to pain, functional decline, and long-term complications. Regular hip surveillance is therefore essential to allow early intervention and prevent progression. At present, screening is performed manually by experienced clinicians, which can be time consuming and costly. This study aimed to compare the performance of artificial intelligence models with expert clinicians in detecting hip dysplasia in children with cerebral palsy. Materials and Methods: A thorough search of Embase, Ovid MEDLINE, and Web of Science was conducted from inception to July 2025. Studies evaluating AI-based detection of hip dysplasia in children aged 18 years or younger with cerebral palsy were included. Risk of bias was assessed using the QUADAS-2 tool. Results were synthesised narratively in accordance with SWiM guidelines. Results: Across the six included studies, which included over 4000 radiographs, AI sensitivity for detecting hip dysplasia ranged from 70% to 97.4%, and specificity ranged from 85% to 96%, depending on the migration percentage thresholds applied. Area under the curve values ranged from 0.923 to 0.999. Only one study performed external validation using a national surveillance dataset. Risk of bias was moderate to high in most studies due to internal validation and small datasets. Conclusions: The findings suggest that AI demonstrates potential as an adjunct for hip surveillance in children with cerebral palsy.
