AI in Hip Arthroplasty Templating: Beyond the Knee
How AI is changing preoperative templating for total hip arthroplasty, automated cup and stem sizing, offset and leg-length planning, and where 3D CT planning outperforms 2D templates.
Key takeaways
Hip arthroplasty templating is moving from 2D acetate-style overlays to AI-assisted 3D planning from CT. The clinical payoff is sizing accuracy: AI-based 3D planning predicts acetabular cup size far more reliably than 2D templating (one study reported ~84% within one size for AI-HIP vs ~64% for 2D templates). That accuracy translates to fewer intraoperative surprises, better offset and leg-length restoration, and leaner implant inventory. The trade-offs are the usual ones, CT dose, data governance, and whether the segmentation is editable.
Why Hip Templating Is Harder Than It Looks
Total hip arthroplasty (THA) success depends on restoring three things: the right cup and stem size, the correct offset, and equal leg length. Getting these wrong means dislocation risk, limp, or revision. Traditional 2D templating, scaling a template over a calibrated radiograph, is quick but limited: it cannot fully capture version, three-dimensional offset, or complex deformity, and calibration errors propagate into sizing errors.
This is why hip is a natural place for 3D, CT-based planning to add value beyond the knee.
What AI Adds to Hip Templating
Automated segmentation and 3D modelling
AI segmentation of the pelvis and proximal femur from CT produces a patient-specific 3D model in minutes rather than hours, making 3D templating viable for routine cases, not just complex revisions.
More reliable size prediction
The headline benefit is sizing. AI models trained on large implant databases predict cup and stem size from the 3D anatomy with accuracy that 2D templating cannot match. Reported results show AI-based 3D planning substantially outperforming 2D templates for size prediction, AI-HIP reached roughly 84% accuracy within one cup size versus about 64% for 2D templates in one comparison.
Offset, version, and leg length
A 3D model lets the planner assess femoral offset, acetabular version, and leg-length restoration in three dimensions, the parameters that 2D templating approximates at best.
Inventory and OR efficiency
Reliable predicted sizing means a hospital can prepare the predicted size plus one above and below, rather than a full tray range, a meaningful operational and cost improvement.
2D vs 3D: When Each Still Fits
2D templating is not obsolete: it is fast, cheap, and adequate for straightforward primary cases with good radiographs. 3D CT-based AI planning earns its place in complex anatomy, dysplasia, revision, deformity, and where sizing certainty matters most. The decision is the same as elsewhere in preoperative planning: match the tool to the case.
The Caveats
- CT dose. 3D planning needs CT; weigh the dose against the planning benefit, especially in younger patients.
- Editable segmentation. As with the knee, automated output must be correctable before it drives sizing.
- Data governance. Cloud planning uploads pelvic CT to external servers; client-side processing avoids that.
- Validation for your population. Sizing models reflect the implant databases and populations they were trained on; confirm relevance to your patients and implants.
From Plan to OR: How the Predicted Size Gets Used
A sizing prediction only matters if it changes what happens in theatre, and a "84% within one size" figure is easy to misread. It does not mean the surgeon implants whatever the model outputs. It means the predicted size is reliable enough to narrow the preparation, not to remove the decision.
In practice the predicted cup and stem size sets the centre of a small bracket. A hospital that trusts the prediction can open the predicted size plus one above and one below, rather than staging a full tray range for every case. That is where the operational savings come from: less instrument reprocessing, smaller consignment inventory, and fewer mid-case calls for a tray that was not opened. The surgeon still confirms the size intraoperatively against trial reduction, stability, and leg length, the prediction has narrowed the search, not made the call.
The same 3D model that drives the size prediction also carries the offset and version plan into the case, which is the part 2D could never deliver. Restoring femoral offset and equalising leg length are read off the 3D reconstruction rather than estimated from a calibrated film. This is the hip-specific instance of a general pattern: a validated reconstruction step feeding a concrete intraoperative plan, the same loop our OJSM-validated PSI work established for the knee, and the same reason the hybrid AI-plus-verify segmentation workflow matters here too: the size prediction is only as good as the bone model it is computed from.
FAQ
Is AI hip templating more accurate than 2D templating? For implant size prediction, yes, studies report AI-based 3D planning markedly outperforming 2D templates (e.g., ~84% vs ~64% within one cup size in one comparison).
Do I need a CT for AI hip planning? 3D AI templating is CT-based. 2D templating uses radiographs. Choose based on case complexity and dose considerations.
Does AI replace the surgeon's plan? No. It produces a suggested size and 3D model the surgeon validates and adjusts; the plan remains the surgeon's.
The Takeaway
Hip is where 3D, AI-assisted templating shows a clear, measurable edge over 2D, mainly in sizing reliability and 3D restoration of offset and leg length. Use it where the case complexity or sizing stakes justify the CT, and keep the segmentation editable and the data governance clean.
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Disclaimer: This article is for educational and research purposes only. Salnus tools are designated for Research Use Only (RUO) and are not cleared medical devices. Mention of third-party tools is for educational context only. Clinical decisions should be made by qualified physicians.
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Reviewed by the Salnus biomedical engineering team.