Z Orthop Unfall 2020; 158(S 01): S66-S67
DOI: 10.1055/s-0040-1717335
Vortrag
DKOU20-307 Grundlagenforschung>28. Bildgebung - Navigation - Robotik

Three-dimensional segmented CT images help orthopedic surgeons and residents to correctly classify proximal humeral fractures

J Dauwe
*   = präsentierender Autor
1   University Hospitals Leuven, AO Research Institute Davos, Leuven
,
K Mys
2   AO Research Institute Davos, Davos-Platz
,
G Putzeys
3   AZ Groeninge, Kortrijk
,
J Schader
2   AO Research Institute Davos, Davos-Platz
,
G Richards
2   AO Research Institute Davos, Davos-Platz
,
B Gueorguiev
2   AO Research Institute Davos, Davos-Platz
,
P Varga
2   AO Research Institute Davos, Davos-Platz
,
S Nijs
4   University Hospitals Leuven, Leuven
› Author Affiliations
 

Objectives Osteosynthesis of proximal humeral fractures remains challenging with a high failure rate reported. This might be due to the complexity of these injuries, the difficulties in the appropriate selection and correct execution of treatment.

Understanding and correct classification of the fracture are important for preoperative planning. Nevertheless, this is a challenging task in case of complex fractures, partially due to difficulties in recognizing their three-dimensional (3D) extent, including the number and dislocation of fragments. Preoperative computed tomography (CT) is a standard procedure in most hospitals, but the contained information may not be fully utilized. Advanced visualization of the CT images may improve the accuracy of fracture classification. The aim of this study was to investigate the feasibility and added value of semi-automatic segmented 3D CT visualizations in proximal humeral fracture classification for observers with two different experience levels: residents and specialized shoulder surgeons.

Methods Seventeen patients from 2015 until 2019 with proximal humeral fractures and treatment codes plate-screw-osteosynthesis and hemiprosthesis with a preoperative CT scan were included in this retrospective study. The CT scans were semi-automatically segmented, indicating every fracture fragment in a different color. Fracture classification ability of 21 orthopedic residents and 12 experienced shoulder surgeons was tested. Both groups were asked to classify the fractures using three different modalities (standard slice-wise CT analysis, conventional 3D CT reconstruction and 3D segmented model) into three different classification systems (Neer, AO/OTA and LEGO). The effect of the modality and the classification system on the classification accuracy was evaluated with 2-way ANOVA.

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Results and Conclusion All participants were able to classify the fractures significantly better using the 3D segmentations (94 % correct answers on average, p < 0.0001) compared with the conventional 3D reconstructions (54 %) and with the standard slice-wise CT analysis (35 %) into the three classification systems. Both observer groups achieved significantly worse classification accuracy in the LEGO system compared to the two others. These results indicate that CT reconstruction enhanced with color-demarcated fragments is an added value in proximal humeral fracture classification since it improves the accuracy of both residents and shoulder surgeons. A better classification accuracy is expected to improve treatment selection and execution; these are to be tested in future studies.

Stichwörter proximal humeral fracture, fracture classification, computed tomography, computer assisted segmentation



Publication History

Article published online:
15 October 2020

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