UNSX-HRNet: Modeling anatomical uncertainty for landmark detection in total hip arthroplasty

Background

Accurate detection of anatomical landmarks from radiographic images is critical for total hip arthroplasty (THA) surgical planning and postoperative evaluation. However, existing methods face significant challenges in unstructured data, such as irregular patient postures or occluded landmarks, which hinder their robustness and reliability. This study aims to develop an advanced deep learning framework to address these challenges, by leveraging uncertainty estimation to handle unstructured data and assigning uncertainty scores to predicted landmarks, thereby alerting clinicians to focus on these results.

Methods

We propose Unstructured X-ray - High-Resolution Net (UNSX-HRNet), a framework that integrates high-resolution networks with uncertainty estimation based on anatomical relationships to predict landmarks without relying on a fixed number of points. The method suppresses low-certainty landmarks to accurately handle unstructured data while highlighting the certainty level of each landmark to provide correction guidance. The model was trained and tested on both structured and unstructured datasets, with performance evaluated using multiple precision metrics.

Results

Experimental results demonstrate that UNSX-HRNet achieves improvement, exceeding 60 % across multiple evaluation metrics when applied to unstructured datasets. On structured datasets, the framework maintains high performance, showcasing its robustness and adaptability across varying data conditions.

Conclusions

UNSX-HRNet offers a reliable and automated solution for THA landmark detection, addressing the challenges of unstructured data through uncertainty-aware predictions. This approach not only improves accuracy but also provides actionable insights for clinicians, contributing to the development of AI-driven expert systems for surgical planning and monitoring.