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Suketu Bhavsar, Bhanu B. Gowda, Maulini Bhavsar | Journal of Paediatrics and Child Health | (2025)

Key Takeaways

Plain English Takeaway

Computer programs can help doctors spot hip problems in children just as well as experts can, but more testing in real-world settings is needed before using them everywhere.

Study Aim

The main goal of this paper is to systematically review how well artificial intelligence (AI) can detect developmental dysplasia of the hip (DDH), a condition where a child's hip joint does not form properly. The authors want to see if AI can match the accuracy of expert doctors when analyzing medical images like X-rays and ultrasounds for DDH diagnosis. Simply put: The study checks if computer programs can find hip problems in kids as well as doctors do.

Study Design

The authors conducted a systematic review, following established guidelines for diagnostic test accuracy studies. They searched several medical and technical databases for studies up to October 2024. The review included 23 studies that used AI to analyze X-ray or ultrasound images of children under 16 years old for DDH. Only studies comparing AI results to expert doctors' diagnoses were included. The authors did not combine results into a meta-analysis because the studies used different methods and tools. Simply put: The researchers looked at many studies where computers and doctors both checked kids' hip images, and compared their results.

Findings

The review found that AI systems can detect DDH on both X-ray and ultrasound images with high accuracy, sensitivity (ability to find true cases), and specificity (ability to rule out non-cases). In many studies, AI matched or even outperformed less experienced doctors and worked much faster than humans. However, the authors note that most studies used high-quality images and controlled settings, so results might not be as strong in real-world clinics. There was also a lot of variation in the types of AI used and the ages of children studied. The authors recommend more research using diverse, real-world data to make sure AI tools work well for all patients and settings. Simply put: Computer programs can spot hip problems in kids very well in studies, but we need more real-life testing to be sure they work everywhere.

Abstract

AI demonstrated comparable effectiveness to physicians in detecting DDH. However, limited evaluation on external datasets restricts its generalisability. Further research incorporating diverse datasets and real-world applications is needed to assess its broader clinical impact on DDH diagnosis.

Referenced In

🍼 Why reading baby hip ultrasounds is harder than you think—and how AI is stepping in to help.

Ever had an ultrasound? Now imagine trying to get a perfect picture of a squirming infant's hip joint while they're crying and moving. That's the reality of screening for developmental dysplasia of the hip (DDH), a condition affecting 1–3% of babies that can cause lifelong disability if missed.

The problem? 🎯 Too few experts, too many babies, and results that vary wildly depending on who's holding the probe.

A new systematic review & meta-analysis by Azmi et al., 2026, just published in Pediatric Radiology, brings together the strongest evidence yet on AI-assisted infant hip ultrasound—and the results are promising:

🔍 Key Findings from Azmi et al. (2026):

  • High diagnostic accuracy: Pooled sensitivity 92% (95% CI 86–95%) and specificity 96% (95% CI 91–98%) across 9 studies with 6,351 hips

    • Compiled 29 studies but analysed only 9 who provided 2×2 data using bivariate random-effects model.

  • Fast to learn: Operators need only ~1–2 hours of training to use AI-assisted systems

  • Quicker scans: Acquisition times drop by 20–50%

🤩 Compared to other reviews like Bhavsar et al., 2025 & Kamath et al., 2026, this meta-analysis analysed raw imaging data rather than final statistical predictions, making this a promising census for DDH. The authors then close the discussion by calling for multi-centre trials - testing that spans across different hospitals and environments.

What's your take? Is AI ready to assist or even replace the expert eye?

Fun Fact🤓: The "Graf method" requires precise positioning and angle measurements that take years to master. Miss the angle by a few degrees, and you miss the diagnosis or even send healthy babies for unnecessary treatments.

  • A 2022 paper by Oelen et al., 2022 claims that AI precision outperforms trained physicians, with an error of 3.9° vs. physician error of 7.1° for alpha angles

If you'd like a comprehensive read on the current trends in paediatric orthopaedic disorders, check out this review on recent developments: Current Trends and Future Directions in the Diagnosis and Management of Pediatric Orthopedic Disorders

Image credit: kidshealth.org

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