# Scaling Early Detection for Developmental Dysplasia of the Hip With Artificial Intelligence-Assisted Imaging

## TL;DR

AI helps detect hip dysplasia in children more accurately and consistently than traditional methods, potentially improving early diagnosis and treatment.

## Contribution

A literature review on AI-assisted diagnosis of DDH, highlighting its diagnostic accuracy and potential for scalable screening.

## Key findings

- AI models show better diagnostic accuracy and lower variability than traditional methods for DDH.
- Segmentation networks and 3D convolutional models improve image analysis and classification in DDH diagnosis.
- AI-powered portable systems could expand DDH screening access in low-resource areas.

## 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.