Comparison between Weightbearing-CT semiautomatic and manual measurements in Hallux Valgus

BackgroundRadiographic measurements are an essential tool to determine the appropriate surgical treatment and outcome for Hallux Valgus (HV). HV deformity is best evaluated by weight-bearing computed tomography (WBCT).The objective was (1) to assess the reliability of WBCT computer-assisted semi-automatic imaging measurements in HV, (2) to compare semi-automatic with manual measurements in the setting of an HV, and (3) to compare semi-automatic measurements between HV and control group.MethodsIn this retrospective IRB (ID# 201904825) approved study, we assessed patients with hallux valgus deformity. The sample size calculation was based on the hallux valgus angle (HVA). Thus to obtain the 0.8 power, including 26 feet with HV in this study, was necessary. Our control group consisted of 19 feet from 19 patients without HV. Raw multiplanar data was evaluated using software CubeVue®. In the axial plane, hallux valgus angle (HVA), intermetatarsal angle (IMA), and interphalangeal angle (IPA) were measured. The semiautomatic 3D measurements were performed using the Bonelogic®Software. Inter-rater reliabilities were performed using ICC. Agreement between methods was tested using the Bland-Altman plots. The difference between Patologic and Control cases using semi-automatic measurements was assessed with the Wilcoxon signed-rank test. Alpha risk was set to 5% (α = 0.05). P ≤ 0.05 were considered significant.ResultsReliabilities utilizing ICC were over 0.80 for WBCT manual measurements and WBCT semi-automatic readings. Inter and intraobserver agreement for Manual and Semi-automatic WBCT measurements demonstrated excellent reliability.ConclusionsSemi-automatic measurements are reproducible and comparable to measurements performed manually. The software differentiated pathological from non-pathological conditions when subjected to semi-automatic measurements. The development of advanced semi-automatic segmentation software with minimal user intervention is essential for the establishment of big data and can be integrated into clinical practice, facilitating decision-making.