Construction and validation of a three-dimensional CT spatial localization algorithm for segond fracture based on...

The clinical translation of AI-assisted surgical navigation and digital twin modeling is hindered by the absence of a standardized, reusable anatomical spatial reference framework, as current one-dimensional or two-dimensional localization methods for the Segond fracture bed inadequately represent three-dimensional bony spatial relationships for intelligent navigation systems.

To meet the needs of AI- and digital twin-driven precision orthopedics, this study constructs a CT-based three-dimensional coordinate system of the knee using tibial landmarks, applies a normalization algorithm for standardized Segond fracture localization, and validates a reusable anatomical reference model, thereby bridging the translational gap in AI localization and offering a standardized computational framework for knee surgical planning and digital twin modeling.

A retrospective review was conducted on 30 patients with left-sided Segond fracture who underwent thin-slice knee CT examinations at Beijing Shunyi Hospital between June 2010 and December 2025. Patients were randomly assigned to a modeling group (n = 15) and an independent validation group (n = 15). Using 3D Slicer software, the following landmarks were annotated: the most lateral point of the fibular head (P1), the midpoint of the lateral edge of the tibial tubercle (P2), the most superior point of Gerdy's tubercle (P3), and the center of the Segond fracture bed (P4). A right-handed orthogonal local coordinate system was constructed with P2 as the origin, based on P1, P2, and P3, and scale normalization was performed using the distance from P2 to P1. The modeling group was used to estimate the preliminary single center anatomical prior of the fracture bed in the normalized tibial local coordinate system(N-TLS). In the validation group, only the native global RAS coordinates of P1, P2, and P3 were input, and the predicted native global RAS coordinates of P4 were obtained via inverse geometric mapping. Localization accuracy was evaluated using the three-dimensional Euclidean distance between the predicted coordinates and the CT-measured reference coordinates.

The mean normalized coordinates of the Segond fracture bed center in the modeling group were (0.8209, 0.4300, -0.0078). Based on this anatomical prior, the deterministic geometric estimator achieved a mean localization error of 3.21 mm in the 15-case single-center independent validation cohort, with a standard deviation of 1.23 mm, a median error of 2.75 mm, a maximum error of 5.33 mm, and a minimum error of 1.68 mm. These findings indicate that the Segond fracture bed center exhibits a population-level distribution pattern within this local coordinate framework that is amenable to geometric localization.

This study established a novel three-dimensional CT-based computational coordinate system for the knee joint using tibial bony landmarks, characterized the standardized three-dimensional spatial distribution pattern of the center of the Segond fracture bed, and developed a geometric prediction algorithm model for the fracture center point. The model demonstrated favorable millimeter-level localization accuracy, providing a standardized computational anatomical reference for digital surgical planning and future knee digital twin modeling, and offering methodological support for evidence-based clinical decision-making and intelligent orthopedic healthcare systems.