AI in Hip OA: Where Deep Learning Meets Clinical Need
AI in hip osteoarthritis — radiographic classification, progression prediction, and Salnus's PROSPERO-registered systematic review.
The Hip OA Problem
Hip osteoarthritis affects over 300 million people globally and is the second most common indication for joint replacement after knee OA. Yet AI research in hip OA lags significantly behind knee OA — a 2025 bibliometric analysis found approximately 4× more published deep learning studies for knee OA than hip OA.
This gap is not due to lower clinical need. Hip OA presents unique diagnostic and prognostic challenges: the ball-and-socket geometry makes radiographic interpretation more complex than the relatively planar knee joint, femoroacetabular impingement (FAI) can mimic or coexist with early OA, and the relationship between radiographic severity and patient symptoms is weaker in the hip than in the knee.
What AI Can Do for Hip OA Today
Radiographic classification is the most developed application. Deep learning models trained on AP pelvis radiographs can classify hip OA severity using the Kellgren-Lawrence grading system — the same 5-point scale used for knee OA, adapted for the hip joint. Published models report accuracies of 70–85% for binary OA detection and 55–70% for multi-class KL grading, comparable to or slightly below knee OA model performance.
The lower accuracy compared to knee models reflects both the geometric complexity of the hip joint and smaller available training datasets. While the Osteoarthritis Initiative (OAI) provides ~36,000 knee radiographs, no equivalent large-scale public hip OA dataset exists.
Joint space width measurement in the hip presents different challenges than in the knee. The spherical geometry of the femoral head means JSW varies around the joint circumference, and the minimum JSW location (superolateral vs. superomedial) carries diagnostic significance. AI models can automate femoral head segmentation and compute JSW at standardised locations, improving measurement reproducibility.
Progression prediction is where hip OA AI may have its greatest clinical impact. Identifying which patients with early radiographic changes will progress to require total hip arthroplasty (THA) within 5–10 years could fundamentally change management strategies — enabling earlier intervention for high-risk patients and avoiding unnecessary treatment for those with stable disease.
The Data Scarcity Challenge
The single largest barrier to hip OA AI development is data. There is no hip equivalent of the Osteoarthritis Initiative (OAI) — no large, publicly available dataset of standardised hip radiographs with expert grading. Most published studies use institutional datasets of 500–3,000 images, which limits model generalisability.
Building clinical partnerships to create multi-institutional hip OA datasets is essential for advancing the field. This requires standardised imaging protocols, consensus grading by multiple experts, and ethical frameworks for data sharing that protect patient privacy while enabling research.
At Salnus, our approach to this challenge mirrors our knee OA strategy: privacy-by-design architecture that processes imaging data client-side, combined with clinical partnerships that generate anonymised research datasets under institutional ethics approval.
Why a Systematic Review Matters Now
The rapid growth in hip OA AI publications creates a critical need to synthesise the evidence. Individual studies use different datasets, classification systems, imaging modalities, and performance metrics — making direct comparison between models nearly impossible without systematic analysis.
At Salnus, we have registered a systematic review and meta-analysis on PROSPERO (the international prospective register of systematic reviews) to comprehensively map the current state of AI in hip osteoarthritis. Our review examines deep learning and machine learning approaches across the full spectrum of hip OA applications: diagnosis, classification, progression prediction, surgical planning, and outcome estimation.
The PROSPERO registration ensures methodological transparency — our search strategy, inclusion criteria, and analysis plan are publicly documented before results are known, eliminating the risk of post-hoc protocol changes that can bias systematic reviews.
Key Questions Our Review Addresses
Diagnostic accuracy: What are the pooled sensitivity and specificity of AI models for hip OA detection and grading? How do these compare to the inter-observer variability among radiologists?
Imaging modalities: Which imaging inputs (AP pelvis radiograph, lateral hip, CT, MRI) yield the best AI performance? Is multi-modal input superior to single-modality?
Methodological quality: Are published studies using appropriate validation strategies (external test sets, not just cross-validation on training data)? What is the risk of bias?
Clinical translation: How many models have moved beyond retrospective validation to prospective clinical testing or regulatory approval?
The Architecture Challenge
Hip OA presents architectural challenges that knee OA models don't face. A standard AP pelvis radiograph contains both hips, the sacrum, and the proximal femora — the model must first localise the hip joints before classifying them. This typically requires a two-stage pipeline: a detection model (often YOLO-based) to identify and crop the hip regions, followed by a classification model (DenseNet, ResNet, or EfficientNet) for OA grading.
Additionally, hip anatomy varies more between patients than knee anatomy (coxa vara/valga, dysplasia, cam/pincer morphology), and the presence of osteophytes at the acetabular rim can be difficult to distinguish from normal labral calcification — a distinction that challenges both human readers and AI models.
From Knee to Hip: Transfer Learning
One promising approach leverages the extensive work done on knee OA. Models pre-trained on large knee OA datasets can be fine-tuned on smaller hip OA datasets — the low-level features (bone texture, joint margin detection, sclerosis patterns) transfer well between joints. This transfer learning approach can partially compensate for the smaller hip OA training datasets.
At Salnus, our knee OA models — developed across 21 experiments on 11 architectures — provide a strong foundation for hip OA model development. The DenseNet-121 architecture that achieved 84.1% accuracy on knee OA binary detection is our starting point for hip OA classification.
Clinical Implications
For practising orthopaedic surgeons, the practical takeaway is that AI-assisted hip OA tools are 2–3 years behind knee OA tools in maturity, but the trajectory is clear. Within the next few years, validated AI tools for hip OA classification, JSW measurement, and progression risk scoring will become available for clinical use.
For hip OA AI to reach clinical utility, the same deployment principles that apply to knee OA screening hold true: the model must integrate into existing workflows, respect patient data privacy through client-side inference, and provide explainable outputs (such as GradCAM heatmaps) that surgeons can verify against their clinical judgement.
The DICOM standard ensures that hip radiographs from any imaging equipment can be processed by AI models — the same infrastructure that supports knee OA analysis extends naturally to hip OA.
Salnus Platform Integration
The Salnus DICOM Viewer is designed as a multi-joint system from the ground up. Our body part detection module automatically identifies the anatomical region from DICOM metadata and imaging content, routing each study to the appropriate analysis pipeline.
The knee OA pipeline — binary screening, KL grading, JSW measurement, alignment analysis — is our current production focus. The hip OA pipeline is next, informed by our systematic review findings and built on the same client-side inference architecture that processes all imaging data locally in the surgeon's browser.
If you are an orthopaedic surgeon working with hip OA patients and interested in early access to our hip analysis tools as they develop, get in touch.
Disclaimer: This article is for educational and research purposes only. AI tools referenced are designated for research use only (RUO) and are not cleared medical devices. Clinical decisions should be made by qualified physicians based on comprehensive patient assessment.
References:
- Murphy NJ, et al. Hip osteoarthritis: etiopathogenesis and implications for management. Adv Ther. 2016;33(11):1921-1946.
- Defined radiographic grading systems for hip OA: Kellgren JH, Lawrence JS. Ann Rheum Dis. 1957; Tönnis D, Heinecke A. Clin Orthop Relat Res. 1999.
- Stable prevalence estimates: Global Burden of Disease Study. Lancet Rheumatol. 2023.
Reviewed by the Salnus biomedical engineering team.