PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping

Abstract

Quantitative analyses of plantar pressure images typically occur at the group level and under the assumption that individuals within each group display homogeneous pressure patterns. When this assumption does not hold, a personalized analysis technique is required. Yet, existing personalized plantar pressure analysis techniques work at the image level, leading to results that can be unintuitive and difficult to interpret. To address these limitations, we introduce PAPPI: the Personalized Analysis of Plantar Pressure Images. PAPPI is built around the statistical modelling of the relationship between plantar pressures in healthy controls and their demographic characteristics. This statistical model then serves as the healthy baseline to which an individual's real plantar pressures are compared using statistical parametric mapping. As a proof-of-concept, we evaluated PAPPI on a cohort of 50 hallux valgus patients. PAPPI showed that plantar pressures from hallux valgus patients did not have a single, homogeneous pattern, but instead, 5 abnormal pressure patterns were observed in sections of this population. When comparing these patterns to foot pain scores (i.e. Foot Function Index, Manchester-Oxford Foot Questionnaire) and radiographic hallux angle measurements, we observed that patients with increased pressure under metatarsal 1 reported less foot pain than other patients in the cohort, while patients with abnormal pressures in the heel showed more severe hallux valgus angles and more foot pain. Also, incidences of pes planus were higher in our hallux valgus cohort compared to the modelled healthy controls. PAPPI helped to clarify recent discrepancies in group-level plantar pressure studies and showed its unique ability to produce quantitative, interpretable, and personalized analyses for plantar pressure images.

Outlier Detection for Foot Complaint Diagnosis: Modeling Confounding Factors Using Metric Learning

“…These measurements are used to validate how well the estimated baselines match real plantar pressure videos from healthy individuals. Finally, the CAD WALK hallux valgus database (http://doi.org/10.5281/zenodo.2598496) is used to evaluate how capable the proposed technique is at identifying the plantar pressure anomalies that are known to exist for this patient population [2]. This dataset consists of 69 hallux valgus cases measured from the feet of 50 patients (19 of these 50 patients have both feet affected).…”

“…Instead, an outlier detection approach is preferred. This is equally the case when assessing foot complaints [2]. When assessing foot complaints, a gait analysis is performed with pressure-sensing plates [11], wearable sensors [1], or cameras [13] recording the patient's walk.…”

“…Outlier detection is therefore desired to identify whether anomalies exist in an individual's plantar pressure video, and if so, when and where. By localizing these abnormalities, it is hoped that a better diagnosis of foot complaints can be achieved [2].…”

“…Patients that are significantly different from this baseline are identified as having an anomaly. For medical imaging applications like our plantar pressure video analysis, outlier detection is regularly combined with statistical parametric mapping (SPM) to localize anomalies in both space and time [2], [4]. SPM-style outlier detection typically follows an established workflow.…”

“…As a result, we have a contextual outlier detection problem where the demographic features define the context under which plantar pressures normality should be judged. While multiple contextual outlier detection techniques exist [2], [8], [15], they are either limited to linear regression models [2], or have yet to show compatibility with the SPM framework. Specifically, SPM requires that the contextual outlier detection algorithm produce a statistical measure (e.g.…”