Background Ankle fractures are common fractures in trauma surgery. Several studies have compared gait patterns between affected patients and control groups. However, no one used the Heidelberg Foot Measurement Method in combination with statistical parametric mapping of the entire gait cycle in this patient cohort. We sought to identify possible mobility deficits in the tibio-talar joint and medial arch in patients after ankle fractures as a sign of stiffness and pain that could result in a pathological gait pattern. We focused on the tibio-talar flexion as it is the main movement in the tibio-talar joint. Moreover, we examined the healing progress over time. Methods Fourteen patients with isolated ankle fractures were included prospectively. A gait analysis using the Heidelberg Foot Measurement Method was performed 9 and 26 weeks after surgery to analyse the tibio-talar dorsal flexion, the foot tibia dorsal flexion, the subtalar inversion and the medial arch as well as the cadence, the walking speed and the ground reaction force. The American Orthopedic Foot & Ankle Society ankle hindfoot score was used to obtain clinical data. Results were compared to those from 20 healthy participants. Furthermore, correlations between the American Orthopedic Foot & Ankle Society hindfoot score and the results of the gait analysis were evaluated. Results Statistical parametric mapping showed significant differences for the Foot Tibia Dorsal Flexion for patients after 9 weeks (53–75%: p = 0.001) and patients after 26 weeks (58–70%: p = 0.011) compared to healthy participants, respectively. Furthermore, significant differences regarding the tibio-talar dorsal flexion for patients 9 weeks after surgery (15–40%: p < 0.001; 56,5–70%: p = 0.007; 82–88%: p = 0.033; 97–98,5%: p = 0.048) as well as patients after 26 weeks (62,5–65%: p = 0.049) compared to healthy participants, respectively. There were no significant differences looking at the medial arch and the subtalar inversion. Moreover, significant differences regarding the ground reaction force were found for patients after 9 weeks (0–17%: p < 0.001; 21–37%: p < 0.001; 41–54%: p < 0.001; 60–64%: p = 0.013) as well as patients after 26 weeks (0–1,5%: p = 0.046; 5–15%: p < 0.001; 27–33%: p = 0.001; 45–49%: p = 0.005; 57–59%: p = 0.049) compared to healthy participants, respectively. In total, the range of motion in the tibio-talar joint and the medial arch was reduced in affected patients compared to healthy participants. Patients showed significant increase of the range of motion between 9 and 26 weeks. Conclusions This study shows, that patients affected by ankle fractures show limited mobility in the tibio-talar joint and the medial arch when compared to healthy participants. Even though the limitation of motion remains at least over a period of 26 weeks, a significant increase can be recognized over time. Furthermore, if we look at the absolute values, the patients’ values tend to get closer to those of the control group. Trial registration This study is registered at the German Clinical Trials Register (DRKS00023379).

Background Clinical gait analysis is a crucial step for identifying foot disorders and surgery planning. However, a large amount of gait data makes this assessment difficult and time-consuming. There are separate efforts to reduce its complexity by manually or automatically finding features (e.g. minimum of a joint angle in a specific axis), identifying the foot condition by Machine Learning (ML), and interpreting the outcome by explainable artificial intelligence (xAI). Methods In this article, we explore the potential of state-of-the-art ML algorithms to automate all these steps for a set of 6 foot conditions. New features are created manually and then recursive feature elimination is employed based on Support Vector Machines (SVM) and Random Forest (RF) to eliminate the features with low variance. SVM, RF, K-nearest Neighbor (KNN), Logistic Regression (LREGR), and Majority Voting (MV) algorithms are compared for classification and Local Interpretable Model-agnostic Explanation (LIME) is used for the interpretation of the outcome of the ML models. 40 features are eliminated and 334 features are given to the classifier models as inputs. Results The foot conditions are classified with a maximum average accuracy of 0.86 by KNN and MV, maximum average recall of 0.97 by KNN, and max average F1 score of 0.86 by KNN and MV. Conclusions High success scores indicate that the relation between the selected features and foot conditions should be strong and meaningful, potentially indicating clinical relevance. All models are interpreted for each foot condition for random 20 patients and the most contributing features are graphically demonstrated. The proposed ML pipeline can be easily extended for other foot conditions and retrained as new data arrives. It can help experts and physicians in the identification of foot conditions and the planning of potential surgeries.

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