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Scaling Early Detection for Developmental Dysplasia of the Hip With Artificial Intelligence-Assisted Imaging
Ibrahim D Al-Obaidi1, Ibrahim K Al Abid2, Abdullah Almazouni2,3
1Medicine, Tawam Hospital, Al Ain, ARE.
Insights
Artificial intelligence (AI) enhances the diagnosis of developmental dysplasia of the hip (DDH) in children. AI models show improved accuracy and consistency over traditional methods, aiding early detection and treatment.
Area of Science:
- Pediatric Musculoskeletal Imaging
- Artificial Intelligence in Medicine
- Orthopedic Diagnostics
Background:
- Developmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition requiring early diagnosis to prevent long-term complications.
- Current diagnostic methods rely on clinical examination and imaging (ultrasound, radiography), which are operator-dependent and prone to measurement errors.
- Artificial intelligence (AI) is emerging as a transformative technology in medical imaging analysis.
Purpose of the Study:
- To review the current literature on AI-assisted diagnosis of DDH using ultrasound and radiographic imaging.
- To summarize the capabilities and limitations of AI in DDH diagnosis.
- To explore the potential of AI in improving screening and diagnosis of DDH.
Main Methods:
- Literature review of studies employing AI for DDH diagnosis.
- Analysis of AI models, including deep learning and convolutional neural networks, for image analysis and parameter measurement (alpha angle, beta angle, acetabular index).
- Evaluation of AI performance metrics such as diagnostic accuracy and interobserver variability.
Main Results:
- AI-based models demonstrate high diagnostic accuracy, consistency, and reduced interobserver variability compared to traditional methods.
- Advanced AI architectures improve image assessment and classification for DDH.
- AI-powered portable ultrasound and cloud platforms offer potential for expanded DDH screening, especially in underserved areas.
Conclusions:
- AI shows promise as a valuable tool to supplement clinical assessment for early, accurate, and scalable DDH diagnosis.
- Challenges remain, including dataset heterogeneity, model generalizability, and clinical workflow integration.
- Further multicenter validation, standardization, and regulatory oversight are crucial for safe clinical translation of AI in DDH diagnosis.
Abstract:
The field of pediatric musculoskeletal imaging is evolving at the moment because of artificial intelligence (AI) and is starting to play an important role in diagnosing developmental dysplasia of the hip (DDH), a common pediatric orthopedic condition that can lead to gait problems, functional issues, pain, and early-onset osteoarthritis if not treated early. Standard diagnostic methods depend on clinical examination and imaging systems, including ultrasound and radiography, that are highly operator-dependent and susceptible to measurement error. Recent developments in AI, including deep learning and convolutional neural networks, have enabled automated image analysis, detecting anatomical landmarks automatically, and accurate measurement of the alpha angle, beta angle, and acetabular index as important diagnostic parameters. This article presents a literature review that summarizes the current literature in the field of AI-assisted DDH diagnosis using ultrasound and radiographic imaging. In the literature, AI-based models exhibit great diagnostic accuracy, better consistency, and lower interobserver variability than traditional evaluation. Advanced architectures, such as segmentation networks and 3D convolutional models, further improve image quality assessment and classification. Portable ultrasound systems and cloud-based diagnostic platforms powered by AI provide hope for expanding access to DDH screening in low-resource settings. These advances are, however, difficult because of dataset heterogeneity, the generalizability of deep learning models across different people and imaging devices, and the interpretability and integration of deep learning models into clinical workflows. Long-term multicenter validation, standardization in reporting, and regulatory control are necessary to guarantee safe clinical translation. The literature generally endorses AI as a useful supplement to the clinician's clinical toolkit; its utilization could potentially offer better early, accurate, and scalable diagnosis of DDH.
