Introduction Musculoskeletal models are important tools for studying movement patterns, tissue loading, and neuromechanics. Personalising bone anatomy within models improves analysis accuracy. Few studies have focused on personalising foot bone anatomy, potentially incorrectly estimating the foot’s contribution to locomotion. Statistical shape models have been created for a subset of foot-ankle bones, but have not been validated. This study aimed to develop and validate statistical shape models of the functional segments in the foot: first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus; then, to assess reconstruction accuracy of these shape models using sparse anatomical data. Methods Magnetic resonance images of 24 individuals feet (age = 28 ± 6 years, 52% female, height = 1.73 ± 0.8 m, mass = 66.6 ± 13.8 kg) were manually segmented to generate three-dimensional point clouds. Point clouds were registered and analysed using principal component analysis. For each bone segment, a statistical shape model and principal components were created, describing population shape variation. Statistical shape models were validated by assessing reconstruction accuracy in a leave-one-out cross validation. Statistical shape models were created by excluding a participant’s bone segment and used to reconstruct that same excluded bone using full segmentations and sparse anatomical data (i.e. three discrete points on each segment), for all combinations in the dataset. Tali were not reconstructed using sparse anatomical data due to a lack of externally accessible landmarks. Reconstruction accuracy was assessed using Jaccard index, root mean square error (mm), and Hausdorff distance (mm). Results Reconstructions generated using full segmentations had mean Jaccard indices between 0.77 ± 0.04 and 0.89 ± 0.02, mean root mean square errors between 0.88 ± 0.19 and 1.17 ± 0.18 mm, and mean Hausdorff distances between 2.99 ± 0.98 mm and 6.63 ± 3.68 mm. Reconstructions generated using sparse anatomical data had mean Jaccard indices between 0.67 ± 0.06 and 0.83 ± 0.05, mean root mean square error between 1.21 ± 0.54 mm and 1.66 ± 0.41 mm, and mean Hausdorff distances between 3.21 ± 0.94 mm and 7.19 ± 3.54 mm. Jaccard index was higher (P < 0.01) and root mean square error was lower (P < 0.01) in reconstructions from full segmentations compared to sparse anatomical data. Hausdorff distance was lower (P < 0.01) for midfoot and calcaneus reconstructions using full segmentations compared to sparse anatomical data. Conclusion For the first time, statistical shape models of the primary functional segments of the foot were developed and validated. Foot segments can be reconstructed with minimal error using full segmentations and sparse anatomical landmarks. In future, larger training datasets could increase statistical shape model robustness, extending use to paediatric or pathological populations.

Background: Femoroacetabular impingement syndrome is characterized by chondrolabral damage and hip pain. The specific biomechanics used by people with femoroacetabular impingement syndrome during daily activities may exacerbate their symptoms. Femoroacetabular impingement syndrome can be treated nonoperatively or surgically; however, differential treatment effects on walking biomechanics have not been examined. Purpose: To compare the 12-month effects of physical therapist–led care or arthroscopy on trunk, pelvis, and hip kinematics as well as hip moments during walking. Study Design: Secondary analysis of multi-centre, pragmatic, two-arm superiority randomized controlled trial subsample; Level of evidence, 1. Methods: A subsample of 43 participants from the Australian Full randomised controlled trial of Arthroscopic Surgery for Hip Impingement versus best cONventional (FASHIoN trial) underwent gait analysis and completed the International Hip Outcome Tool (iHOT-33) at both baseline and 12 months after random allocation to physical therapist–led care (personalized hip therapy; n = 22; mean age 35; 41% female) or arthroscopy (n = 21; mean age 36; 48% female). Changes in trunk, pelvis, and hip biomechanics were compared between treatment groups across the gait cycle using statistical parametric mapping. Associations between changes in iHOT-33 and changes in hip kinematics across 3 planes of motion were examined. Results: As compared with the arthroscopy group, the personalized hip therapy group increased its peak hip adduction moments (mean difference = 0.35 N·m/body weight·height [%] [95% CI, 0.05-0.65]; effect size = 0.72; P = .02). Hip adduction moments in the arthroscopy group were unchanged in response to treatment. No other between-group differences were detected. Improvements in iHOT-33 were not associated with changes in hip kinematics. Conclusion: Peak hip adduction moments were increased in the personalized hip therapy group and unchanged in the arthroscopy group. No biomechanical changes favoring arthroscopy were detected, suggesting that personalized hip therapy elicits greater changes in hip moments during walking at 12-month follow-up. Twelve-month changes in hip-related quality of life were not associated with changes in hip kinematics.

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