AI Model Flags Infant Hip Dysplasia Before Ultrasound Using Simple Clinical Signs

Every year, thousands of infants are born with hips that never quite fit together the way they should, a condition known as developmental dysplasia of the hip, or DDH. The spectrum ranges from a shallow acetabulum, the socket that should cradle the femoral head, to full dislocation. Caught early, most cases resolve with simple splinting; caught late, the condition can saddle a child with abnormal hip mechanics and, decades later, early joint replacement. The diagnostic dilemma has always been one of resources: universal ultrasound screening of every baby catches more cases but risks overtreating hips that would have matured on their own, while selective screening based on risk factors can miss infants who slip through the cracks. A new study from Shenzhen Children’s Hospital, published in Frontiers in Digital Health, offers a computational middle path: a machine learning model that weighs twelve clinical and epidemiological variables to tell clinicians which infants genuinely need an ultrasound scan.

The research team, led by Shuang-shuang Zhang and colleagues spanning institutions in China and Italy, assembled an unusually large dataset for this kind of problem. Between January 2020 and December 2022, 12,105 infants underwent DDH screening at Shenzhen Children’s Hospital, each receiving a Graf-method hip ultrasound, the current gold standard for early detection, alongside a standardized physical examination performed by an orthopedic surgeon with more than twenty years of experience. Of those infants, 207, roughly 1.7 percent, were diagnosed with DDH, a prevalence consistent with the wide geographic and ethnic variation reported in the literature, which ranges from about 0.1 percent in African populations to 7.6 percent among Native Americans.

Raw data of this kind presents a classic statistical trap. Because DDH prevalence falls sharply as infants age, a naive model trained on the full cohort would learn to associate older age with lower risk, essentially memorizing the age distribution rather than the biology. The researchers countered this with age-stratified undersampling, drawing 11,898 DDH-negative infants down to match the age profile of the 207 positive cases. They then generated two balanced training cohorts, one with equal numbers of positive and negative cases and another with twice as many negatives, and split each into training and held-out test sets at an 8:2 ratio with fixed random seeds for reproducibility. Hyperparameter tuning and ten-fold cross-validation happened exclusively on the training data, keeping the test set blind until final evaluation.

Before any machine learning began, the team ran a clinical prescreening. Eighteen candidate variables went through univariate chi-square and t-tests, followed by multivariate logistic regression, a deliberate constraint to keep model complexity low given the modest number of positive cases and to anchor the final model to predictors clinicians already recognize. Six variables fell away, including birth weight, winter birth, cesarean delivery, and multiple births, leaving twelve: infant age, female sex, first-degree family history of DDH, premature birth, firstborn status, breech presentation, oligohydramnios, swaddling, frog-leg positioning, skin-fold asymmetry, restricted hip abduction, and leg length discrepancy. The logistic regression itself was revealing. A first-degree family history carried an odds ratio of nearly 42, by far the largest effect, while restricted hip abduction and leg length discrepancy each raised odds roughly tenfold or elevenfold. Frog-leg positioning or vertical holding, conversely, was protective, halving the odds, a finding that aligns with the mechanical theory that hips held in extension are deprived of the positioning that drives socket development.

Six algorithms competed for the top spot: random forest, support vector classification, logistic regression, k-nearest neighbors, LightGBM, and XGBoost, a gradient-boosted decision tree method known for both accuracy and speed. On the 1:1 balanced data, XGBoost won decisively, posting an area under the receiver operating characteristic curve of 0.883, accuracy of 0.807, precision of 0.744, an F1 score of 0.800, sensitivity of 0.865, and specificity of 0.761. The choice of the 1:1 cohort over the 1:2 cohort was not arbitrary. Models trained on the 1:2 data achieved higher specificity, peaking at 0.910, but their sensitivity topped out at 0.750, because the extra negative cases pushed the algorithms toward predicting negative outcomes. Given that a missed DDH diagnosis carries serious consequences for hip development, the researchers deliberately favored sensitivity, accepting more false positives in exchange for fewer missed cases.

The most striking result came from external validation. The team tested the XGBoost model on 80 infants screened at Jinhua Municipal Central Hospital in 2024, a completely separate institution, with 40 positive and 40 negative cases. Far from degrading, performance improved: the AUC rose to 0.909, accuracy to 0.838, precision to 0.829, and the F1 score to 0.840, with sensitivity of 0.850 and specificity of 0.825. This kind of cross-hospital transferability is the acid test for clinical prediction models, many of which quietly overfit to the quirks of a single dataset. The authors are careful to frame this as preliminary evidence, noting that the validation sample was small and drawn from a tertiary hospital, but the direction of the result is encouraging.

What elevates this work above a black-box classifier is its interpretability layer. The team applied SHAP, Shapley additive explanations, a technique borrowed from cooperative game theory that quantifies exactly how much each variable pushes an individual prediction up or down. Across the model, five variables dominated: asymmetry of thigh and groin skin folds, restriction of hip abduction, swaddling, breech presentation, and female sex. Two of the top five are pure physical examination findings, which the authors read as a directive: hands-on clinical assessment during infant checkups deserves at least as much attention as the demographic checklist. The SHAP summary also confirmed that swaddling, which holds the hips in prolonged extension, actively increases predicted risk, reinforcing a growing body of evidence, including a randomized controlled trial of traditional Mongolian swaddling, that holding practices shape hip development.

The model’s positioning within the broader artificial intelligence landscape is worth noting. Most AI research on DDH targets the diagnostic stage itself, using deep learning to read ultrasound images or pelvic radiographs automatically. Those systems require imaging equipment and trained operators. This model operates upstream, before any imaging, integrating demographic risk factors with bedside findings to produce a continuous, individualized risk estimate. Compared with earlier risk tools, such as a logistic regression calculator that reached an AUC of 0.81 without external validation, or a risk-factor counting rule that refers infants for ultrasound when two or more factors are present, the XGBoost approach offers graded prioritization rather than a binary threshold, potentially valuable where ultrasound capacity is limited.

The authors are candid about limitations. The cohort consisted of infants whose parents actively sought screening, introducing potential selection bias, and the retrospective design precludes causal inference. The 207 positive cases constrained how complex the models could be, and infants with syndromic, pathological, paralytic, or teratologic dislocations were excluded, so the model cannot be generalized to those subpopulations. A subtle methodological caveat involves the variable prescreening, which was performed on the full cohort before train-test splitting, a practice that can leak information; nested cross-validation with fold-specific feature selection would have been more rigorous but was impractical given the sample size. Perhaps most importantly, because both the internal test sets and the external validation set were artificially balanced, the predicted probabilities do not reflect the true 1.7 percent population prevalence. The authors explicitly recommend treating the model as a relative-risk ranking tool for prioritizing ultrasound referrals, not as a source of calibrated absolute risk, and they call for prospective cohorts with natural prevalence to evaluate calibration and decision-curve performance.

Even with those caveats, the study sketches a plausible future for infant hip screening. A clinician armed with twelve routinely collected data points, from family history to the symmetry of skin folds, could generate an individualized risk score in seconds and reserve scarce ultrasound slots for the infants most likely to need them. If prospective, multicenter validation confirms what this preliminary evidence suggests, the humble physical examination, augmented by gradient-boosted trees and game-theoretic explanations, may become the first line of defense against a condition that, untreated, follows children into adulthood.

Subject of Research: Machine learning prediction of developmental dysplasia of the hip in infants using clinical risk factors

Article Title: Machine learning–based personalized prediction of developmental dysplasia of the hip using risk factors and clinical examination

Article References: Zhang, S.-S., Caggiari, G., Chen, X.-Y., Huang, B.-X., Deng, H.-S., Xu, N., & Shi, W. (2026). Machine learning–based personalized prediction of developmental dysplasia of the hip using risk factors and clinical examination. Frontiers in Digital Health, 8, Article 1912106. https://doi.org/10.3389/fdgth.2026.1912106

Image Credits: AI Generated

DOI: 10.3389/fdgth.2026.1912106

Keywords: developmental dysplasia of the hip, machine learning, XGBoost, SHAP, infant screening, hip ultrasound, pediatric orthopedics, risk prediction, clinical examination, Graf method, gradient boosting, digital health