The accuracy of statistical shape models in predicting bone shape: A systematic review

BackgroundThis systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling. MethodsA systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible. Results2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error). ConclusionStatistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.

Automated summary

This systematic review investigates the accuracy of predicting 3D models from 2D imaging using statistical shape modelling. Out of 2127 papers screened, 34 studies were included for data extraction. The results showed that the best achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error). Therefore, statistical shape modelling can accurately predict detailed 3D anatomical models from limited 2D imaging.