Pedographic classification and ulcer detection in the diabetic foot

Waldecker, Ute

doi:10.1016/j.fas.2011.03.004

Cited by 20 publications

(20 citation statements)

References 16 publications

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“…Specific to diabetic foot pressures, the prior work of Waldecker (2012) and See et al (2010) focused on the identification of ulcer risk and patient classification. A previous study (De Cock et al, 2006) investigated plantar pressures during jogging on a limited number of foot regions with the number of clusters set to four.…”

Section: Discussionmentioning

confidence: 99%

“…New patients could then be classified into one of these groups and the group-specific footwear solution could be prescribed with the expectation that it would reduce corresponding elevated plantar pressures. Waldecker (2012) successfully used logistic regression with plantar pressure, force and pressure-time integral to predict the risk of ulceration although the validity of the pressure-time calculation in this and other studies has been challenged (Waaijman and Bus, 2012; Melai et al, 2011). Acharya et al (2008, 2011) and See et al (2010) used principal component analysis and artificial neural networks to classify patients as normal or diabetic, with and without neuropathy.…”

Section: Introductionmentioning

confidence: 94%

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Clustering and classification of regional peak plantar pressures of diabetic feet

Bennetts

Owings

Erdemir

et al. 2013

Journal of Biomechanics

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High plantar pressures have been associated with foot ulceration in people with diabetes, who can experience loss of protective sensation due to peripheral neuropathy. Therefore, characterization of elevated plantar pressure distributions can provide a means of identifying diabetic patients at potential risk of foot ulceration. Plantar pressure distribution classification can also be used to determine suitable preventive interventions, such as the provision of an appropriately designed insole. In the past, emphasis has primarily been placed on the identification of individual focal areas of elevated pressure. The goal of this study was to utilize k-means clustering analysis to identify typical regional peak plantar pressure distributions in a group of 819 diabetic feet. The number of clusters was varied from 2 to 10 to examine the effect on the differentiation and classification of regional peak plantar pressure distributions. As the number of groups increased, so too did the specificity of their pressure distributions: starting with overall low or overall high peak pressure groups and extending to clusters exhibiting several focal peak pressures in different regions of the foot. However, as the number of clusters increased, the ability to accurately classify a given regional peak plantar pressure distribution decreased. The balance between these opposing constraints can be adjusted when assessing patients with feet that are potentially “at risk” or while prescribing footwear to reduce high regional pressures. This analysis provides an understanding of the variability of the regional peak plantar pressure distributions seen within the diabetic population and serves as a guide for the preemptive assessment and prevention of diabetic foot ulcers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Clustering and classification of regional peak plantar pressures of diabetic feet

Bennetts

Owings

Erdemir

et al. 2013

Journal of Biomechanics

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“…Treating diabetic foot ulcers requires a multidisciplinary approach to optimize care in these complex patients . There are often several potential treatment options, from local wound care and IV antibiotics to surgical care including debridement or amputation .…”

mentioning

confidence: 99%

Analyzing treatment aggressiveness and identifying high‐risk patients in diabetic foot ulcer return to care

Remington

Hernandez‐Boussard

Warstadt

et al. 2016

Wound Repair Regeneration

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Rates of diabetes and its associated comorbidities have been increasing in the United States, with diabetic foot ulcer treatment representing a large cost to the patient and healthcare system. These ulcers often result in multiple hospital admissions. This study examined readmissions following inpatient care for a diabetic foot ulcer and identified modifiable factors associated with all-cause 30-day readmissions to the inpatient or emergency department (ED) setting. We hypothesized that patients undergoing aggressive treatment would have lower 30-day readmission rates. We identified patient discharge records containing International Classification of Disease ninth revision codes for both diabetes mellitus and distal foot ulcer in the State Inpatient and Emergency Department databases from the Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project in Florida and New York, 2011-2012. All-cause 30-day return to care visits (ED or inpatient) were analyzed. Patient demographics and treatment characteristics were evaluated using univariate and multivariable regression models. The cohort included 25,911 discharges, having a mean age of 63 and an average of 3.8 comorbidities. The overall rate of return to care was 30%, and 21% of subjects underwent a toe or midfoot amputation during their index stay. The most common diagnosis codes upon readmission were diabetes mellitus (19%) and infection (13%). Patients with a toe or midfoot amputation procedure were less likely to be readmitted within 30 days (odds ratio: 0.78; 95% confidence interval: 0.73, 0.84). Presence of comorbidities, black and Hispanic ethnicities, and Medicare and Medicaid payer status were also associated with higher odds of readmission following initial hospitalization (p < 0.05). The study suggests that there are many factors that affect readmission rates for diabetic foot ulcer patients. Understanding patients at high-risk for readmission can improve counseling and treatment strategies for this fragile patient population.

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“…More recently, machine learning algorithms have been employed in order to classify an individual's plantar pressure measurement into patient or healthy control groups [25][26][27][28][29]. In these studies, a database of plantar pressure data is combined with the corresponding group memberships in order to define a non-linear regression function between the two quantities.…”

Section: Midfoot Mt 1-2 Mt 3-5 Hallux Toes 2-5mentioning

confidence: 99%

“…In these studies, a database of plantar pressure data is combined with the corresponding group memberships in order to define a non-linear regression function between the two quantities. A variety of machine learning algorithms have been used to perform this regression, from artificial neural networks [25,27], to logistic regression [29], to nearest neighbour classification [28], to support vector machines [26]. Regardless of the algorithm used, the resulting classifier produces a personalized result: an individual's plantar pressure measurement, as a whole, gets labelled as either healthy or unhealthy.…”

Section: Midfoot Mt 1-2 Mt 3-5 Hallux Toes 2-5mentioning

confidence: 99%

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

et al. 2020

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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.

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Pedographic classification and ulcer detection in the diabetic foot

Cited by 20 publications

References 16 publications

Clustering and classification of regional peak plantar pressures of diabetic feet

Clustering and classification of regional peak plantar pressures of diabetic feet

Analyzing treatment aggressiveness and identifying high‐risk patients in diabetic foot ulcer return to care

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

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