Biomechanical and functional alterations in the diabetic foot: Differences between type I and type II Diabetes

Caravaggi, Paolo; Berti, Lisa; Leardini, Alberto; Lullini, Giada; Marchesini, Giulio; Baccolini, Luca; Giacomozzi, Claudia

doi:10.1016/j.gaitpost.2017.07.063

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Cited by 4 publications

(2 citation statements)

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“…also reported that midfoot and forefoot have higher peak pressure and peak time integral in a group of adults with diabetes. [ 19 ] Caravaggi et al . also give emphasize that plantar pressure distribution over the forefoot is associated with types of diabetes and the course and severity of diseases.…”

Section: Discussionmentioning

confidence: 99%

Plantar pressure distribution among diabetes and healthy participants: A cross-sectional study

2021

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Section: Discussionmentioning

confidence: 99%

Plantar pressure distribution among diabetes and healthy participants: A cross-sectional study

2021

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“…The American Diabetes Association (ADA) proposed CAD (Coronary Artery Disease) screening of type II diabetic patients with two or more additional risk factors, complications, or EKG changes indicating CAD and the presence of carotid or peripheral arterial disease in 1998 [6]. It was also said that the EKG stress test was insensitive [8].…”

Section: Introductionmentioning

confidence: 99%

Dp-Rnn: Type Ii Diabetic Prediction Using Gkfcm And Rnn

Mrs.K.Gandhimathi¹

2021

TURCOMAT

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Diabetes is a form of metabolic disorder marked by elevated persistent blood glucose (BG), leading to several severe problems in the long term. Continuous monitoring and prediction of BG concentration are needed to help diabetic patients maintain their wellbeing. If insulin is minimized, machine learning models, such as CNN, RNN, and others, are standard data-driven BG prediction solutions. They use several patients' BG data to train the prediction model. However, all of the training data with the same parameters can not accurately capture BG fluctuation characteristics. Motivated by the possibility that various subgroups of diabetic patients have different BG fluctuation trends, we suggest a new BG prediction method called DP-RNN focused on recurrent neural networks (RNN) and incorporates a clustering pre-process using GKFCM. In terms of BG estimation precision, numerical finding shows that the suggested DP-RNN methodology uses more than one cluster for type II diabetes and outperforms Logistic regression (LR) and other CNN approaches.

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Prevalence of ankle equinus and correlation with foot plantar pressures in people with diabetes

Searle¹,

Spink²,

Chuter

2018

Clinical Biomechanics

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Biomechanical and functional alterations in the diabetic foot: Differences between type I and type II Diabetes

Cited by 4 publications

References 3 publications

Plantar pressure distribution among diabetes and healthy participants: A cross-sectional study

Plantar pressure distribution among diabetes and healthy participants: A cross-sectional study

Dp-Rnn: Type Ii Diabetic Prediction Using Gkfcm And Rnn

Prevalence of ankle equinus and correlation with foot plantar pressures in people with diabetes

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