Advanced CT visualization improves the accuracy of orthopaedic trauma surgeons and residents in classifying proximal humeral fractures: a feasibility study.

IF 2.2
Jan Dauwe, Karen Mys, Guy Putzeys, Jana F Schader, R Geoff Richards, Boyko Gueorguiev, Peter Varga, Stefaan Nijs
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引用次数: 4

Abstract

Purpose: Osteosynthesis of proximal humeral fractures remains challenging with high reported failure rates. Understanding the fracture type is mandatory in surgical treatment to achieve an optimal anatomical reduction. Therefore, a better classification ability resulting in improved understanding of the fracture pattern is important for preoperative planning. The purpose was to investigate the feasibility and added value of advanced visualization of segmented 3D computed tomography (CT) images in fracture classification.

Methods: Seventeen patients treated with either plate-screw-osteosynthesis or shoulder hemi-prosthesis between 2015 and 2019 were included. All preoperative CT scans were segmented to indicate every fracture fragment in a different color. Classification ability was tested in 21 orthopaedic residents and 12 shoulder surgeons. Both groups were asked to classify fractures using three different modalities (standard CT scan, 3D reconstruction model, and 3D segmented model) into three different classification systems (Neer, AO/OTA and LEGO).

Results: All participants were able to classify the fractures more accurately into all three classification systems after evaluating the segmented three-dimensional (3D) models compared to both 2D slice-wise evaluation and 3D reconstruction model. This finding was significant (p < 0.005) with an average success rate of 94%. The participants experienced significantly more difficulties classifying fractures according to the LEGO system than the other two classifications.

Conclusion: Segmentation of CT scans added value to the proximal humeral fracture classification, since orthopaedic surgeons were able to classify fractures significantly better into the AO/OTA, Neer, and LEGO classification systems compared to both standard 2D slice-wise evaluation and 3D reconstruction model.

先进的CT可视化提高骨科创伤外科医生和住院医师对肱骨近端骨折分类的准确性:一项可行性研究。
目的:肱骨近端骨折的植骨术仍然具有挑战性,报道的失败率很高。了解骨折类型是外科治疗中实现最佳解剖复位的必要条件。因此,更好的分类能力有助于提高对骨折类型的了解,这对于术前规划非常重要。目的探讨三维分割CT图像高级可视化在骨折分类中的可行性及附加价值。方法:选取2015年至2019年接受钢板-螺钉-骨固定术或肩关节半假体治疗的患者17例。所有术前CT扫描被分割,以不同的颜色显示每个骨折碎片。对21名骨科住院医师和12名肩关节外科医生进行分类能力测试。两组均被要求使用三种不同的模式(标准CT扫描、3D重建模型和3D分段模型)将骨折分类为三种不同的分类系统(Neer、AO/OTA和LEGO)。结果:与2D切片评估和3D重建模型相比,所有参与者在评估分段三维(3D)模型后都能够更准确地将骨折分类为所有三种分类系统。结论:CT扫描的分割增加了肱骨近端骨折分类的价值,因为与标准的2D切片评估和3D重建模型相比,骨科医生能够更好地将骨折分类为AO/OTA, Neer和LEGO分类系统。
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