Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs
arXiv:2606.15250v1 Announce Type: cross Abstract: Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty. Traditional measurement methods are manual and time-consuming, while recent machine learning approaches typically rely on locating a fixed set of anatomical landmarks. This dependence limits flexibility and may require re-annotation when clinical definitions change. To address this, we propose an automated workflow using Implicit Neural Shape Functions (INSF). Rather than relying on explicit landmark c
The continuous advancements in AI and neural implicit representations are enabling more sophisticated and automated medical imaging analysis. This paper demonstrates a novel application of these techniques in a critical medical diagnostic area.
This development allows for more efficient and potentially more accurate diagnostic processes in orthopedics, reducing the reliance on manual labor and fixed anatomical landmarks. It signifies further automation of medical analysis through advanced AI.
Traditional manual and landmark-dependent radiographic assessment methods for lower-limb alignment could be replaced or significantly augmented by automated, landmark-free approaches using Implicit Neural Shape Functions.
- · Orthopedic surgeons
- · Medical AI developers
- · Healthcare providers
- · Patients needing lower-limb assessment
- · Traditional manual measurement providers
- · Legacy medical imaging software
Automated, more precise, and faster diagnosis of lower-limb alignment conditions becomes widely accessible.
Reduced healthcare costs and improved patient outcomes due to earlier and more accurate interventions for orthopedic issues.
The methodology could generalize to other diagnostic imaging, accelerating overall medical AI integration and development of fully autonomous diagnostic tools.
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Read at arXiv cs.AI