New diabetic foot classification needed

NewsBot · Jul 25, 2022

Prediction of diabetic foot amputation using newly revised DIRECT coding system: Comparison of accuracy with that of five existing classification systems
Da Woon Lee,Si Hyun Kwak,Jun Hyuk Kim,Hwan Jun Choi
10 July 2022

Diabetes mellitus (DM) causes various complications over time, one such complication is diabetic foot ulcers (DFU), which are challenging to treat and can lead to amputation. Additionally, a system for accurate prediction of amputation has yet to be developed. In total, 131 patients were included in the study after retrospectively collecting data from 2016 to 2020 about DFU. The collected data were used for comparison of the accuracy between five existing classification systems and the newly revised DIRECT coding system, and investigation of risk factors for lower extremity amputation (LEA). The existing five classification systems and DIRECT system can effectively predict LEA. The DIRECT3 system has three elements, C-reactive protein (CRP), ulcer history (UH), and hypertension (HTN) in addition to those of the DIRECT system. It had a high predictive value and accuracy similar to that of Wagner and University of Texas (UT) on depth among the five classification systems. Among the statistically significant risk factors, duration of DM and HTN, haemoglobin (Hb), CRP, and UH showed an association with LEA. The DIRECT coding system is effective for predicting LEA and explaining appropriate treatment methods for DFU, and is widely applicable because of its user accessibility and convenience.
Click to expand...

NewsBot · Aug 9, 2022

Classification of diabetic foot ulcer using convolutional neural network (CNN) in diabetic patients
Mawaddah Harahap etal
Aug 1, 2022

The image of chronic wounds on human skin tissue has the similar look in shape, color and size to each other even though they are caused by different diseases. Diabetic ulcer is a condition where peripheral arterial blood vessels are disrupted due to hyperglycemia in people with diabetes mellitus. This research was aimed to analyze the accuracy of the Convolutional Neural Network algorithm in classifying diabetic ulcer disease with a transfer learning approach based on the appearance of the image of the wound on the sole in people with diabetes mellitus. By applying the transfer learning approach, the results showed that the Resnet152V2 model achieved the best accuracy value of 0.993 (99%), precision of 1.00, recall of 0.986, F1-Score of 0.993 and Support of 72. Therefore, the ResNet152V2 model was highly considered for classifying diabetic ulcer in patients with diabetes melitus.
Click to expand...

NewsBot · Aug 9, 2022

Bottom-up approach to build a ‘precision’ risk factor classification for diabetic foot ulcer healing. Proof-of-concept
FloraMbel et al
Diabetes Research and Clinical Practice

Aims
Diabetic foot ulcers (DFU) have a complex multifactorial pathophysiology. It is crucial to identify essential prognostic variables to streamline therapeutic actions and quality-of-care audits. Although SINBAD and University of Texas (UT), the most frequently used prognostic classification systems, were prospectively validated, not all individual parameters were shown to have consistent associations with healing. In this study, we used a bottom-up approach relying on robust methods to identify independent predictors of DFU healing.

Methods
1,664 DFU patients were included by 34 Belgian diabetic foot clinics (DFCs). Twenty-one patient- and foot-related characteristics were recorded at presentation. Predictors of healing were identified using multivariable Cox proportional hazard regression. Multivariable models were built using backward regression with multiple imputation of missing values and bootstrapping.

Results
Five essential independent variables were identified: presentation delay, history of minor amputation, ulcer location, surface area and ischemia. This 5 variable-model showed a better performance compared to models based on existing classification systems.

Conclusions
A bottom-up approach was used to build a prognostic classification for DFU healing based on large databases. It offers new insights and allows to tailor the classification to certain clinical settings. These 5 parameters could be used as a ‘precision classification’ for specialized DFCs.
Click to expand...

NewsBot · Aug 29, 2022

A comprehensive review of methods based on deep learning for diabetes-related foot ulcers
Jianglin Zhang et al
Front Endocrinol (Lausanne). 2022 Aug 8

Background: Diabetes mellitus (DM) is a chronic disease with hyperglycemia. If not treated in time, it may lead to lower limb amputation. At the initial stage, the detection of diabetes-related foot ulcer (DFU) is very difficult. Deep learning has demonstrated state-of-the-art performance in various fields and has been used to analyze images of DFUs.

Objective: This article reviewed current applications of deep learning to the early detection of DFU to avoid limb amputation or infection.

Methods: Relevant literature on deep learning models, including in the classification, object detection, and semantic segmentation for images of DFU, published during the past 10 years, were analyzed.

Results: Currently, the primary uses of deep learning in early DFU detection are related to different algorithms. For classification tasks, improved classification models were all based on convolutional neural networks (CNNs). The model with parallel convolutional layers based on GoogLeNet and the ensemble model outperformed the other models in classification accuracy. For object detection tasks, the models were based on architectures such as faster R-CNN, You-Only-Look-Once (YOLO) v3, YOLO v5, or EfficientDet. The refinements on YOLO v3 models achieved an accuracy of 91.95% and the model with an adaptive faster R-CNN architecture achieved a mean average precision (mAP) of 91.4%, which outperformed the other models. For semantic segmentation tasks, the models were based on architectures such as fully convolutional networks (FCNs), U-Net, V-Net, or SegNet. The model with U-Net outperformed the other models with an accuracy of 94.96%. Taking segmentation tasks as an example, the models were based on architectures such as mask R-CNN. The model with mask R-CNN obtained a precision value of 0.8632 and a mAP of 0.5084.

Conclusion: Although current research is promising in the ability of deep learning to improve a patient's quality of life, further research is required to better understand the mechanisms of deep learning for DFUs.
Click to expand...

NewsBot · Dec 14, 2022

The SHID wound classification system for diabetic foot ulcer patients: a validity study
Suriadi Jais et al
J Med Life. 2022 Oct;15(10):1224-1228

This study aimed to evaluate the predictive validity of the SHID (Suriadi, Haryanto, Imran dan Defa) wound classification system compared to TU (Texas University) and Wagner wound classification systems in Indonesia. A prospective cohort study included patients with diabetic foot ulcers at Kitamura wound clinic in Indonesia. A total of 111 diabetic foot ulcer patients were assessed with SHID, TU, and Wagner wound classification systems. Two postgraduate nursing students assessed 111 wounds of bedside patients and observed them for 4 weeks. The predictive validity test indicated that the cut-off score of ≤grade 2 for SHID was 74% and 97%, ≤grade IB for TU was 77% and 92%, then ≤grade 2 for Wagner was 84% and 71% for sensitivity and specificity, respectively. The area under the curve (AUC) in SHID, TU, and Wagner tools was 0.90 (95% CI: 0.828-0.950), 0.85 (95% CI: I0.766-0.910), and 0.86 (95% CI: 0.775-0.917), respectively. The Youden index for SHID, TU, and Wagner was 0.72%, 0.70%, and 0.55%, respectively. The wound classification systems are good tools for identifying diabetic foot ulcers. However, the newly developed SHID tool produced the best AUC and Youden Index values compared to the Wagner tool.
Click to expand...

NewsBot · Jan 23, 2023

A Novel Classification for Diabetic Foot Ulcers of the First Ray
Eran Tamir et al
Adv Skin Wound Care. 2023 Jan 1;36(1):30-34

Objective: Diabetic foot ulcers of the first ray result from several distinct biomechanical mechanisms related to anatomical deformities, which must be addressed if surgical offloading is contemplated. The objective of this study was to create a classification of the anatomical deformities of first-ray ulcers that could lead to better standardization of treatment and reporting.

Methods: The authors performed a file review of patients with diabetic neuropathy diagnosed with first-ray ulcers over a period of 3 years in an outpatient setting. Anatomical deformities were diagnosed clinically and reported with ulcer location. The primary classification was the metatarsophalangeal joint, the interphalangeal joint, and the distal phalanx.

Results: Records for 59 patients (mean age, 62 years) with University of Texas A1 and A2 ulcers were reviewed. Mean ulcer duration was 2 months. The more common deformities were hallux valgus (41%), hallux valgus interphalangeus (14%), and hallux malleus (20%), and these were primarily associated with metatarsophalangeal, interphalangeal joint, and tip-of-toe ulcers, respectively (P < .0001).

Conclusions: By classifying ulcer locations, the relevant corrective surgery may be chosen. Although some prophylactic procedures may be safe and effective, prophylactic hallux valgus correction in the diabetic foot requires further study.
Click to expand...

NewsBot · Feb 14, 2023

Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification
Imran Chowdhury Dipto, Bill Cassidy, Connah Kendrick, Neil D. Reeves, Joseph M. Pappachan, Vishnu Chandrabalan & Moi Hoon Yap
Diabetic Foot Ulcers Grand Challenge: 12 February 2023

This research conducts an investigation on the effect of visually similar images within a publicly available diabetic foot ulcer dataset when training deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degrade network performance. However, the effect of visually similar non-identical images is an under-researched topic, and has so far not been investigated in any diabetic foot ulcer studies. We use an open-source fuzzy algorithm to identify groups of increasingly similar images in the Diabetic Foot Ulcers Challenge 2021 (DFUC2021) training dataset. Based on each similarity threshold, we create new training sets that we use to train a range of deep learning multi-class classifiers. We then evaluate the performance of the best performing model on the DFUC2021 test set. Our findings show that the model trained on the training set with the 80% similarity threshold images removed achieved the best performance using the InceptionResNetV2 network. This model showed improvements in F1-score, precision, and recall of 0.023, 0.029, and 0.013, respectively. These results indicate that highly similar images can contribute towards the presence of performance degrading bias within the Diabetic Foot Ulcers Challenge 2021 dataset, and that the removal of images that are 80% similar from the training set can help to boost classification performance.
Click to expand...

NewsBot · May 15, 2023

Guidelines on the classification of foot ulcers in people with diabetes (IWGDF 2023 update)
Matilde Monteiro-Soares et al
Diabetes Metab Res Rev. 2023 May 14

Background: This publication represents a scheduled update of the 2019 guidelines of the International Working Group of the Diabetic Foot (IWGDF) addressing the use of systems to classify foot ulcers in people with diabetes in routine clinical practice. The guidelines are based on a systematic review of the available literature that identified 28 classifications addressed in 149 articles and, subsequently, expert opinion using the GRADE methodology.

Methods: First, we have developed a list of classification systems considered as being potentially adequate for use in a clinical setting, through the summary of judgements for diagnostic tests, focussing on the usability, accuracy and reliability of each system to predict ulcer-related complications as well as use of resources. Second, we have determined, following group debate and consensus, which of them should be used in specific clinical scenarios. Following this process, in a person with diabetes and a foot ulcer we recommend: (a) for communication among healthcare professionals: to use the SINBAD (Site, Ischaemia, Bacterial infection, Area and Depth) system (first option) or consider using WIfI (Wound, Ischaemia, foot Infection) system (alternative option, when the required equipment and level of expertise is available and it is considered feasible) and in each case the individual variables that compose the systems should be described rather than a total score; (b) for predicting the outcome of an ulcer in a specific individual: no existing system could be recommended; (c) for characterising a person with an infected ulcer: the use of the IDSA/IWGDF classification (first option) or consider using the WIfI system (alternative option, when the required equipment and level of expertise is available and it is considered as feasible); (d) for characterising a person with peripheral artery disease: consider using the WIfI system as a means to stratify healing likelihood and amputation risk; (e) for the audit of outcome(s) of populations: the use of the SINBAD score.

Conclusions: For all recommendations made using GRADE, the certainty of evidence was judged, at best, as being low. Nevertheless, based on the rational application of current data this approach allowed the proposal of recommendations, which are likely to have clinical utility
Click to expand...

NewsBot · Aug 1, 2023

Evaluating Classification Systems of Diabetic Foot Ulcer Severity: A 12-Year Retrospective Study on Factors Impacting Survival
Otilia Niță et al
Healthcare (Basel). 2023 Jul 20;11(14):2077

(1) Background: This study examines the survival of patients after their first presentation with diabetic foot ulcers (DFUs) to the regional Diabetes, Nutrition, and Metabolic Diseases Clinic within the Emergency Clinical Hospital "Sf. Spiridon", Iaşi, and analyzes the factors associated with this outcome. (2) Methods: In this retrospective study, patients with DFUs consecutively referred between 1 January 2007 and 31 December 2017 were followed up until 31 December 2020 (for 13 years). The study group included 659 subjects. (3) Results: During the study period, there were 278 deaths (42.2%) and the average survival time was 9 years. The length of hospitalization, diabetic nephropathy, chronic kidney disease, glomerular filtration rate, cardiovascular disease, hypertension, anemia, and DFU severity were the most significant contributors to the increase in mortality. Patients with severe ulcers, meaning DFUs involving the tendon, joint, or bone, had a higher mortality risk than those with superficial or pre-ulcerative lesions on initial presentation (Texas classification HR = 1.963, 95% CI: 1.063-3.617; Wagner-Meggitt classification HR = 1.889, 95% CI: 1.024-3.417, SINBAD Classification System and Score HR = 2.333, 95% CI: 1.258-4.326) after adjusting for confounding factors. (4) Conclusions: The findings of this study suggested that patients presenting with severe ulcers involving the tendon, joint, or bone exhibited a significantly higher risk of mortality, even when potential confounders were taken into consideration.
Click to expand...

NewsBot · Aug 10, 2023

Are current wound classifications valid for predicting prognosis in people treated for limb-threatening diabetic foot ulcers?
Ya-Chu Chang et al
Int Wound J. 2023 Aug 9

This study aims to investigate whether the current wound classifications were valid for the treatment prognosis of subjects treated for limb-threatening diabetic foot ulcers (LTDFU). A total of 1548 patients with LTDFU and infection were studied, with wounds recorded using the Wagner, Texas, PEDIS and WIfI classifications while major lower extremity amputations (LEAs) or in-hospital mortality incidences were defined as poor outcomes. Among them, 153 (9.9%) patients received major LEAs and 38 (2.5%) patients died. After adjustments, the Wagner classification and Texas stage as well as clinical factors such as comorbidity with major adverse cardiac events (MACE), being under dialysis and having serum levels of C-reactive protein (CRP) and albumin were independent factors for prognosis. For patients without dialysis, Wagner and Texas stage stood out independently for prognosis. For patients on dialysis, only levels of CRP (odds ratio [OR] = 2.2 in Wagner, OR = 2.0 in WIfI, OR = 2.2 in Texas, OR = 2.3 in PEDIS) and albumin (OR = 0.4 in four classifications) were valid predictors. The Wagner system and Texas stage were valid for predicting prognosis in treatment for LTDFUs, suggesting a role of vascular perfusion. MACE history, levels of CRP and albumin level should assist in prediction; more significantly, only levels of CRP and albumin appeared valid for those subjects undergoing dialysis.
Click to expand...

NewsBot · Sep 11, 2023

Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification
Abdullah Alqahtani et al
Diagnostics (Basel). 2023 Sep 1;13(17):2831

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance.
Click to expand...

NewsBot · Oct 26, 2023

The Validity and Reliability of the SINBAD Classification System for Diabetic Foot Ulcers
Jonathan D Brocklehurst
Adv Skin Wound Care. 2023 Nov 1;36(11):1-5

Diabetic foot ulcers (DFUs) are a serious and costly complication of diabetes mellitus with a global prevalence of 6.3% and cost of £8,800 per unhealed DFU in the National Health Service. The three main types of DFU are neuropathic, ischemic, and neuroischemic, with an estimated prevalence of 35%, 15%, and 50%, respectively. Because 85% of lower-limb amputations in patients with diabetes are preceded by a DFU, the task of reducing the current and future burden of DFUs on an international level is of crucial importance. Classification of a DFU is an important and complex process with many independent variables that influence the wound severity. Correct classification of a DFU is important to prevent deterioration in the short term and lower-limb amputation in the long term. Both the accuracy of the clinician's interpretation of categorical data from a classification model and grasp of contextual risk factors can refine diagnoses. The term SINBAD is an acronym for six independent variables: site, ischemia, neuropathy, bacterial infection, area, and depth. This system uses comprehensive parameters with strict criteria to facilitate quick and accurate clinical decisions to prevent lower-limb amputation. In addition to providing quantitative measurement, SINBAD also spotlights the multifaceted characteristics of DFUs. By evaluating the validity and reliability of the SINBAD classification system, its applicability for the assessment of DFUs and prevention of lower-limb amputation can be better understood.
Click to expand...

NewsBot · Nov 27, 2023

Automatic foot ulcer segmentation using conditional generative adversarial network (AFSegGAN): A wound management system
Jishnu P et al
PLOS Digit Health. 2023 Nov 6;2(11)

Effective wound care is essential to prevent further complications, promote healing, and reduce the risk of infection and other health issues. Chronic wounds, particularly in older adults, patients with disabilities, and those with pressure, venous, or diabetic foot ulcers, cause significant morbidity and mortality. Due to the positive trend in the number of individuals with chronic wounds, particularly among the growing elderly and diabetes populations, it is imperative to develop novel technologies and practices for the best practice clinical management of chronic wounds to minimize the potential health and economic burdens on society. As wound care is managed in hospitals and community care, it is crucial to have quantitative metrics like wound boundary and morphological features. The traditional visual inspection technique is purely subjective and error-prone, and digitization provides an appealing alternative. Various deep-learning models have earned confidence; however, their accuracy primarily relies on the image quality, the dataset size to learn the features, and experts' annotation. This work aims to develop a wound management system that automates wound segmentation using a conditional generative adversarial network (cGAN) and estimate the wound morphological parameters. AFSegGAN was developed and validated on the MICCAI 2021-foot ulcer segmentation dataset. In addition, we use adversarial loss and patch-level comparison at the discriminator network to improve the segmentation performance and balance the GAN network training. Our model outperformed state-of-the-art methods with a Dice score of 93.11% and IoU of 99.07%. The proposed wound management system demonstrates its abilities in wound segmentation and parameter estimation, thereby reducing healthcare workers' efforts to diagnose or manage wounds and facilitating remote healthcare.
Click to expand...

NewsBot · Jan 12, 2024

A novel assessment, diagnostic and treatment system for diabetic foot
Jordi Viadé et al
J Foot Ankle Res. 2023 Nov 25;16(1):84

Background: This report aims to present a novel system for the management of foot lesions in patients with diabetes. It was developed in the diabetic foot unit (DFU) of the Mutua de Terrassa University Hospital (HUMT) by primary care professionals, the Diabetic Foot Clinic (DFC), and during emergency cases treated by our group based on daily activities in patients with neuropathy or neuroischemia. BODY: This system considers five degrees of action based on two fixed variables: presence of infection and lesion depth. These two variables allowed the user to investigate aspects of the system until the overall action required by the pathology is made clear. These variables establish pathology stages of various severities that require different actions in aspects of care, management and treatment.

Conclusion: This tool facilitates diagnosis, treatment, and coordination among different members of a multidisciplinary team working in specialized hospital units, primary care centers, and emergency settings.
Click to expand...

NewsBot · Feb 26, 2024

A Systematic Review and Classification of Factors Influencing Diabetic Foot Ulcer Treatment Adherence, in Accordance With the WHO Dimensions of Adherence to Long-Term Therapies
Gwyneth Wy Ng et al
Int J Low Extrem Wounds. 2024 Feb 20

Purpose: Effective treatment of diabetic foot ulcers (DFUs) involves a multidisciplinary treatment plan to promote wound healing and prevent complications. Given the lack of consensus data on the factors affecting patient adherence, a systematic review was performed to identify and classify factors according to the WHO Dimensions of Adherence to Long-Term Therapies.

Methods: Six hundred and forty-three articles from PubMed, Embase, and Scopus were reviewed. The inclusion criteria included qualitative and quantitative studies which discussed factors affecting patient adherence to DFU treatment, had study populations that comprised patients with either prior history of or existing DFU, and had either prior history of DFU treatment or were currently receiving treatment. Factors, and associated measures of adherence, were extracted and organized according to the WHO Dimensions of Adherence to Long-Term Therapies.

Results: Seven quantitative and eight qualitative studies were included. Eleven patient-related factors, seven condition-related factors, three therapy-related factors, five socioeconomic factors, and five health system-related factors were investigated by the included studies. The largest proportion of factors studied was patient-related, such as patient insight on DFU treatment, patient motivation, and patient perception of DFU treatment. There was notable overlap in the range of discussed factors across various domains, in the socioeconomic (including social support, income, social and cultural acceptability of DFU therapy, cost) and therapy-related domains (including duration of treatment, offloading footwear, and reminder devices). Different studies found that specific factors, such as gender and patients having a low internal locus of control, had differing effects on adherence on different cohorts.

Conclusion: Current literature presents heterogeneous findings regarding factors affecting patient adherence. It would be useful for future studies to categorize factors as such to provide more comprehensive understanding and personalized care to patients. Further research can be done to explore how significant factors can be addressed universally across different cohort populations in different cultural and socioeconomic contexts.
Click to expand...

NewsBot · Apr 11, 2024

Diabetic foot ulcers segmentation challenge report: Benchmark and analysis
Moi Hoon Yap et al
Med Image Anal. 2024 Mar 24

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation.
Click to expand...

New diabetic foot classification needed

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

Classification of Diabetic Foot Ulcers

Is the Eichenholtz classification still valid for the diabetic Charcot foot?

Validation of a Diabetic Foot Infection Classification System

Pravibismane for diabetic foot infections

Homeopathy for Diabetic Foot Ulcers

Diabetic Foot Influencers on Twitter

Low Plasma Arginine among Patients with Diabetic Foot Ulcer

Share This Page

New diabetic foot classification needed

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

NewsBot The Admin that posts the news.

Classification of Diabetic Foot Ulcers

Is the Eichenholtz classification still valid for the diabetic Charcot foot?

Validation of a Diabetic Foot Infection Classification System

Pravibismane for diabetic foot infections

Homeopathy for Diabetic Foot Ulcers

Diabetic Foot Influencers on Twitter

Low Plasma Arginine among Patients with Diabetic Foot Ulcer

Share This Page

Useful Searches