A Hybrid Physical--Digital Framework for Annotated Fracture Reduction Data Evaluated using Clinically Relevant 3D metrics

TL;DR

This paper introduces a hybrid physical-digital framework for generating annotated fracture reduction data, enabling realistic evaluation of automatic reduction algorithms using clinically relevant 3D metrics, addressing a key data scarcity challenge.

Contribution

It presents a novel hybrid framework combining physical 3D printing and digital analysis to create annotated fracture reduction datasets with clinically relevant metrics.

Findings

Achieved significant improvements in fracture reduction metrics compared to preoperative measurements.

Successfully evaluated the framework on 11 clinical cases with two independent operators.

Provided a reproducible method for assessing fracture reduction quality using 3D metrics.

Abstract

A major bottleneck in Computer-Assisted Preoperative Planning (CAPP) for fracture reduction is the limited availability of annotated data. While annotated datasets are now available for evaluating bone fracture segmentation algorithms, there is a notable lack of annotated data for the evaluation of automatic fracture reduction methods. Obtaining precise annotations, which are essential for training and evaluating automatic CAPP algorithm, of the reduced bone therefore remains a critical and underexplored challenge. Existing approaches to assess reduction methods rely either on synthetic fracture simulation which often lacks realism, or on manual virtual reductions, which are complex, time-consuming, operator-dependant and error-prone. To address these limitations, we propose a hybrid physical-digital framework for generating annotated fracture reduction data. Based on fracture CTs, fragments are first 3D printed, physically reduced, fixed and CT scanned to accurately recover transformation matrix applied to each fragment. To quantitatively assess reduction quality, we introduce a reproducible formulation of clinically relevant 3D fracture metrics, including 3D gap, 3D step-off, and total gap area. The framework was evaluated on 11 clinical acetabular fracture cases reduced by two independent operators. Compared to preoperative measurements, the proposed approach achieved mean improvements of 168.85 mm 2 in total gap area, 1.82 mm in 3D gap, and 0.81 mm in 3D step-off. This hybrid physical--digital framework enables the efficient generation of realistic, clinically relevant annotated fracture reduction data that can be used for the development and evaluation of automatic fracture reduction algorithms.