Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification? - 18/08/17

Doi : 10.1016/j.otsr.2017.03.018

M. Boudissa ^a,^b,^⁎ , B. Orfeuvre ^a, M. Chabanas ^b, J. Tonetti ^a,^b
^a Service de chirurgie orthopédique et traumatologique, hôpital Nord, université Grenoble Alpes, CHU Grenoble, boulevard de la Chantourne, 38700 La Tronche, France
^b CNRS UMR 5525, laboratoire TIMC-IMAG, pavillon Taillefer, université Grenoble Alpes, 38700 La Tronche, France

^⁎Corresponding author at: Service de chirurgie orthopédique et traumatologique, hôpital Nord, université Grenoble Alpes, CHU Grenoble, boulevard de la Chantourne, 38700 La Tronche, France.

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Abstract

Background

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users.

Hypothesis

Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents.

Methods

Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi² test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility.

Results

Correct classification rates were significantly higher in the “segmentation” group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21–35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84).

Conclusion

Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training.