Table 1.
Summary of the results of group-level plantar pressure analyses of hallux valgus patients.
Fig 1.
The positioning of PAPPI with respect to competing plantar pressure analysis techniques.
The proposed PAPPI technique is unique in providing a highly personalized result (i.e. one result per foot) while also localizing plantar pressure abnormalities to a high degree (i.e. pixel-by-pixel abnormality identification).
Fig 2.
Flow chart of statistical modelling workflow.
The proposed PAPPI technique begins by creating an anatomically-unbiased template to which all healthy peak pressure images are aligned. Once aligned, statistical models are built pixel-by-pixel in order to provide localized statistical analysis. Specifically, the images and their corresponding demographic factors are used to build (a) linear regression models of the peak pressures and (b) Normal distributions of the model residuals.
Fig 3.
Flow chart of statistical testing workflow.
Given a peak pressure image and demographic characteristics from a new patient, a healthy peak pressure image for the patient is estimated using the statistical model. The patient’s measured pressures are then aligned to the estimated image, and a residual image is created by subtracting the estimated pressures from the measured ones. Finally, statistical parametric mapping is used with single-sample t-tests in order to identify patient residuals that are outliers from the statistical model’s Normal distributions over the residuals.
Fig 4.
Given a patient’s peak pressure image (a), it is aligned (b) to the peak pressure image predicted for this patient by the statistical model (c). The aligned (blue) and predicted (red) images are superimposed (d) to ensure that an accurate alignment between them has been achieved. Once aligned, single-sample t-statistics are computed at each pixel (e) and random field theory is used to test for significance (f).
Fig 5.
Patients with increased MT 1 pressures.
Out of the 69 hallux valgus cases we examined, these 26 displayed abnormally high peak pressures under metatarsal 1 (38%).
Fig 6.
Patients with abnormal pressures under the toes.
Out of the 69 hallux valgus cases we examined, these 25 displayed abnormally high peak pressures under toes 2-5 and, occasionally, abnormally low pressures under the hallux (36%).
Fig 7.
Patients with pes planus pressure patterns.
Out of the 69 hallux valgus cases we examined, these 24 displayed abnormally high peak pressures under the midfoot (35%). These abnormality patterns have previously been seen in individuals with pes planus [47, 48].
Fig 8.
Patients with abnormal pressures under MT 2-5.
Out of the 69 hallux valgus cases we examined, these 16 displayed abnormal peak pressures under metatarsals 2-5 (23%).
Fig 9.
Patients with abnormal pressures under heel.
Out of the 69 hallux valgus cases we examined, these 13 displayed abnormal peak pressures under the heel (19%).
Fig 10.
Significant t-test results for Manchester-Oxford Foot Questionnaire Scores (MOXFQ).
Hallux valgus patients with abnormally high pressures under metatarsal 1 showed lower foot pain scores on the MOXFQ than those who did not (p = 0.011). Conversely, patients with abnormal heel pressures showed higher foot pain scores on the MOXFQ than those who did not (p = 0.014). After performing a false discovery rate correction, both results lose statistical significance (α = 0.002).
Fig 11.
Significant t-test results for hallux valgus angles.
Hallux valgus patients with abnormal heel pressures showed higher hallux valgus angles than those who did not (p = 0.033). Conversely, patients that showed no pressure abnormalities had lower hallux valgus angles than those who did (p = 0.018). After performing a false discovery rate correction, both results lose statistical significance (α = 0.002).
Fig 12.
Normality of residuals in statistical model.
P-values from Kolmogorov-Smirnov tests that evaluate the goodness of fit for the normal distributions over the residuals in our statistical model. Note that only 2.8% of the pixels in the model reject this hypothesis (corrected α = 0.0014). The image region within the statistical model is outlined in red.