Predicting the mechanical Hip-Knee-Ankle angle accurately from standard knee radiographs, a cross-validation experiment in 100 patients

Background and purpose

Being able to predict the hip-knee-ankle angle (HKAA) from standard knee radiographs allows studies on malalignment in cohorts lacking full-limb radiography. We aimed to develop an automated image analysis pipeline to measure the femoro-tibial angle (FTA) from standard knee radiographs and test various FTA definitions to predict the HKAA.

Patients and methods

We included 110 pairs of standard knee and full-limb radiographs. Automatic search algorithms found anatomic landmarks on standard knee radiographs. Based on these landmarks, the FTA was automatically calculated according to nine different definitions (six described in the literature and three newly developed). Pearson and intra-class correlation coefficient (ICC)) were determined between the FTA and HKAA as measured on full-limb radiographs. Subsequently, the top four FTA definitions were used to predict the HKAA in a five-fold cross-validation setting.

Results

Across all pairs of images, the Pearson correlations between FTA and HKAA ranged between 0.83 and 0.90. The ICC values from 0.83 to 0.90. In the cross-validation experiments to predict the HKAA, these statistics only decreased minimally. The mean absolute error for the best method to predict the HKAA from standard knee radiographs was 1.8° (± 1.3° SD).

Conclusion

We showed that the HKAA can be automatically predicted from standard knee radiographs with a fair accuracy and very high correlation compared to the true HKAA. Therefore, this method enables research of the relationship between malalignment and knee pathology in large (epidemiological) studies lacking full-limb radiography.