Prediction of diabetic foot ulceration using spatial and temporal dynamic plantar pressure

INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients.It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health problem faced by people of all age groups, identifying foot ulcers at an early stage is essential. In this context, an efficient model to predict the foot ulcer accurately was proposed in this work. OBJECTIVES: To predict DFU using an effective neural network algorithm on a suitable dataset that consists of risk factors and clinical outcomes of the disease. METHODS: In recent days, ML techniques are most commonly used for predicting various diseases. To achieve the objectives a neural network technique, namely extreme learning machine (ELM) is proposed to predict DFU accurately. In addition, three existing algorithms, namely KNN, SVM with Gaussian kernel and ANN are also considered. These are implemented in R programming. RESULTS: Algorithms compared in terms of five evaluation metrics accuracy, zero-one loss, threat score/critical success index (TS/CSI), false omission rate (FOR) and false discovery rate (FDR). The values of accuracy, 0-1 loss, TS/CSI, FOR and FDR obtained for ELM are 96.15%, 0.0385, 0.95, 0 and 0.05 respectively. CONCLUSION: After comparison, it was discovered that ELM had outperformed other algorithms in terms of all the metrics. Thus, it was recommended to use ELM over other algorithms while predicting diabetic foot ulcers.

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“…Ahmed [13] [25] presented their study on the prediction of diabetic foot ulcers. This was done based on dynamic pressure distribution.…”

Section: Literature Surveymentioning

confidence: 99%

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Reddy¹,

Mahesh²,

Nanjundan³

2021

EAI Endorsed Trans Perv Health Tech

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“…One study used 255 patients' genetic data [22] and three studies used bacterial samples taken from DFUs [23]- [25]. Of the remaining 33 studies, four studies used thermographic images [26]- [29], and 22 studies included colour images [3], [17], [18], [30]- [48], three studies included both colour and thermographic images [49]- [51], two papers recorded dynamic foot pressure [32], [52], one study used hyperspectral imaging (HIS) [22] and one study examined 301 patient records for variables affecting amputation decisions [53]. Fifteen of the 37 studies focussed on using ML techniques for classification [3], [23]- [27], [29]- [32], [36]- [38], [53], [54].…”

Section: A Study Characteristicsmentioning

confidence: 99%

“…Of the remaining 33 studies, four studies used thermographic images [26]- [29], and 22 studies included colour images [3], [17], [18], [30]- [48], three studies included both colour and thermographic images [49]- [51], two papers recorded dynamic foot pressure [32], [52], one study used hyperspectral imaging (HIS) [22] and one study examined 301 patient records for variables affecting amputation decisions [53]. Fifteen of the 37 studies focussed on using ML techniques for classification [3], [23]- [27], [29]- [32], [36]- [38], [53], [54]. Eight studies focussed on the segmentation of colour images [3], [17], [40], [42], [43], [45], [46], [48], and four studies focussed on the segmentation of thermal images [28], [49]- [51].…”

Section: A Study Characteristicsmentioning

confidence: 99%

“…Wannous et al[47] classified wound images into granulation, slough and necrosis tissue types to classify the healing stage using SVM, achieving an overall accuracy of 88%.Goyal et al (2020) achieved an accuracy of 90.3% and 72.7% for ischaemia classification and infection classification respectively using a CNN[18]. One study classified patients into either a healthy control group, diabetic control group, or diabetic neuropathy group, achieving an average accuracy of classification of 95%[32]. InYang et al (2018) hyperspectral imaging (HSI) was combined with principle component analysis (PCA) and compared to an algorithm that used oxygen saturation (SpO2) to classify the risk of non-healing in DFUs[54].…”

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confidence: 99%

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Machine Learning in the Prevention, Diagnosis and Management of Diabetic Foot Ulcers: A Systematic Review

2020

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Diabetic foot ulcers (DFUs) are a serious complication for people with diabetes. They result in increased morbidity and pressures on health system resources. Developments in machine learning (ML) offer an opportunity for improved care of individuals at risk of DFUs, to identify and synthesise evidence about the current uses and accuracy of ML in the interventional care and management of DFUs, and, to provide a reference for areas of future research. PubMed, Google Scholar, Web of Science and Scopus were searched using the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for papers involving ML and DFUs. In order to be included, studies needed to mention ML, DFUs, and report relevant outcome measures regarding ML algorithm accuracy. Bias in included studies was assessed using the quality assessment tool for diagnostic accuracy (QUADAS-2). 37 out of 3769 papers were included after applying eligibility criteria. Included papers reported accuracy measures for multiple types of ML algorithms in DFU studies. Whilst varying across the ML algorithm used, all studies reported at least 90% accuracy compared to gold standards using a minimum of one reported ML algorithm for processing or recording data. Applications where ML had positive effects on DFU data analysis and outcomes include image segmentation and classification, raw data analysis and risk assessment. ML offers an effective and accurate solution to guide analysis and procurement of data from interventions which are designed for the care of DFUs in small samples and study conditions. Current research is limited, and, for the development of more applicable ML algorithms, future research should address the following: direct comparison of ML applications with current standards of care, health economic analyses and large scale data collection. There is currently no evidence to confidently suggest that ML methods in DFU diagnosis are ready for implementation and use in healthcare settings.

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“…In the field of sports, more and more researchers have paid attention to the variation in PP distribution induced by the changing sports pattern [5][6][7]. PP measurement and analysis are also meaningful in the design and purchase of sneakers [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Image Classification Algorithm for Plantar Pressure Based on Convolutional Neural Network

Zou¹,

Zhang²,

Ma³

2021

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Human structure-based plantar pressure (PP) analysis has been widely used in medical, sports, footwear design, and footwear sales. The current studies mostly focus on the development of PP measuring technologies and the analysis of pressure distribution features based on sensing results. Relatively few scholars have tried to analyze PP through image processing. To bridge the gap, this paper tries to classify PP images based on convolutional neural network (CNN). Firstly, the authors prepared the zoning and center calculation for PP images, and established a PP image classification model. Then, the PP image features were selected dynamically based on sparse, low-redundancy feature subsets, and the results of principal component analysis (PCA) were combined with the CNN to realize dynamic extraction of features from PP images. Finally, an image classification algorithm was designed based on the inter-area difference in PP distribution. The proposed algorithm was proved feasible through experiments. The research findings provide a reference for processing pressure images in other scenarios.

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Prediction of diabetic foot ulceration using spatial and temporal dynamic plantar pressure

Cited by 23 publications

References 18 publications

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Machine Learning in the Prevention, Diagnosis and Management of Diabetic Foot Ulcers: A Systematic Review

An Image Classification Algorithm for Plantar Pressure Based on Convolutional Neural Network

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