scholarly journals Segmentation Approaches for Diabetic Foot Disorders

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 934
Author(s):  
Natalia Arteaga-Marrero ◽  
Abián Hernández ◽  
Enrique Villa ◽  
Sara González-Pérez ◽  
Carlos Luque ◽  
...  
Keyword(s):  
Diabetic Foot ◽  
Spatial Information ◽  
Ground Truth ◽  
Diabetic Patients ◽  
Non Invasive ◽  
Depth Images ◽  
Accuracy And Precision ◽  
Technical Issues ◽  
Monitoring Protocols ◽  
Visual Light

Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.

scholarly journals Diabetic Foot Infections: The Diagnostic Challenges

2020 ◽  
Vol 9 (6) ◽  
pp. 1779
Author(s):  
Chiara Lauri ◽  
Antonio Leone ◽  
Marco Cavallini ◽  
Alberto Signore ◽  
Laura Giurato ◽  
...  
Keyword(s):  
Diabetic Foot ◽  
Soft Tissues ◽  
Multimodality Imaging ◽  
Diabetic Patients ◽  
Diagnostic Challenge ◽  
Non Invasive ◽  
Diabetic Foot Infections ◽  
Invasive Test ◽  
Appropriate Treatment ◽  
The Moment

Diabetic foot infections (DFIs) are severe complications of long-standing diabetes, and they represent a diagnostic challenge, since the differentiation between osteomyelitis (OM), soft tissue infection (STI), and Charcot’s osteoarthropathy is very difficult to achieve. Nevertheless, such differential diagnosis is mandatory in order to plan the most appropriate treatment for the patient. The isolation of the pathogen from bone or soft tissues is still the gold standard for diagnosis; however, it would be desirable to have a non-invasive test that is able to detect, localize, and evaluate the extent of the infection with high accuracy. A multidisciplinary approach is the key for the correct management of diabetic patients dealing with infective complications, but at the moment, no definite diagnostic flow charts still exist. This review aims at providing an overview on multimodality imaging for the diagnosis of DFI and to address evidence-based answers to the clinicians when they appeal to radiologists or nuclear medicine (NM) physicians for studying their patients.

scholarly journals Accuracy and precision of non-invasive continuous haemoglobin concentration monitoring in diabetic patients

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Rosanna Carmela De Rosa ◽  
Antonio Romanelli
Keyword(s):  
Linear Regression Analysis ◽  
Haemoglobin Concentration ◽  
Diabetic Patients ◽  
Altman Analysis ◽  
Bland Altman Analysis ◽  
Non Invasive ◽  
Accuracy And Precision ◽  
Size Population ◽  
Diabetic Population ◽  
First Time

Abstract Background Accuracy and precision of non-invasive continuous haemoglobin concentration (SpHb) provided by Masimo device in diabetic patients is poorly studied. This retrospective analysis aimed to provide data on SpHb accuracy and precision in diabetic patients. Results The sample size population consisted of 14 patients, with 56 SpHb/Lab data pairs. Lab value showed a mean ± standard deviation (SD) of 13.2 ± 1.2 g/dL, whilst SpHb showed a mean ± SD of 11.8 ± 1.1 g/dL. Linear regression analysis between Lab/SpHb data pairs showed a r of 0.8960 (CI95% 0.8281-0.9379, p value < 0.0001). SpHb underestimated the real Hb values provided by Lab. Bland-Altman analysis showed that SpHb accuracy was −1.37 g/dL (CI95% −1.51 to −1.22 g/dL, p value < 0.0001), precision of 0.55 g/dL, lower LOA −2.45 g/dL (CI95% −2.71 to −2.20 g/dL) and upper LOA −0.28 g/dL (CI95% −0.53 to −0.02 g/dL). Conclusions For the first time, we provided data on SpHb accuracy and precision in the diabetic population. SpHb showed a high correlation coefficient when compared with Lab values, but the wide LOA limits its accuracy.

Care and prevention of diabetic foot: perceptions of diabetic patients

2016 ◽  
pp. 7-14 ◽  
Author(s):  
Andréia Caroline Fernandes Salgueiro ◽  
Elane Fabíola de Sousa Jerônimo da Silva ◽  
Verônica Bidinotto Brito ◽  
Gustavo Orione Puntel ◽  
Vanderlei Folmer
Keyword(s):  
Diabetic Foot ◽  
Diabetic Patients

.

Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System

2020 ◽  
Vol 16 (4) ◽  
pp. 397-426
Author(s):  
Muhammad Nadeem Ashraf ◽  
Muhammad Hussain ◽  
Zulfiqar Habib
Keyword(s):  
Diabetic Retinopathy ◽  
Blood Vessels ◽  
Early Stage ◽  
Spherical Shape ◽  
Cost Effective ◽  
Lesion Detection ◽  
Diabetic Patients ◽  
Cad System ◽  
Non Invasive ◽  
Optic Discs

Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.

scholarly journals The Effects of Silver-Releasing Foam Dressings on Diabetic Foot Ulcer Healing

2021 ◽  
Vol 10 (7) ◽  
pp. 1495
Author(s):  
Yu-Chi Wang ◽  
Hsiao-Chen Lee ◽  
Chien-Lin Chen ◽  
Ming-Chun Kuo ◽  
Savitha Ramachandran ◽  
...  
Keyword(s):  
Wound Healing ◽  
Diabetic Foot ◽  
Healing Rate ◽  
Early Period ◽  
Foot Ulcer ◽  
Diabetic Foot Ulcer ◽  
Wound Management ◽  
Control Group ◽  
Diabetic Patients ◽  
High Morbidity

Diabetic foot ulcers (DFUs) are a serious complication in diabetic patients and lead to high morbidity and mortality. Numerous dressings have been developed to facilitate wound healing of DFUs. This study investigated the wound healing efficacy of silver-releasing foam dressings versus silver-containing cream in managing outpatients with DFUs. Sixty patients with Wagner Grade 1 to 2 DFUs were recruited. The treatment group received silver-releasing foam dressing (Biatain® Ag Non-Adhesive Foam dressing; Coloplast, Humlebaek, Denmark). The control group received 1% silver sulfadiazine (SSD) cream. The ulcer area in the silver foam group was significantly reduced compared with that in the SSD group after four weeks of treatment (silver foam group: 76.43 ± 7.41%, SSD group: 27.00 ± 4.95%, p < 0.001). The weekly wound healing rate in the silver foam group was superior to the SSD group during the first three weeks of treatment (p < 0.05). The silver-releasing foam dressing is more effective than SSD in promoting wound healing of DFUs. The effect is more pronounced in the initial three weeks of the treatment. Thus, silver-releasing foam could be an effective wound dressing for DFUs, mainly in the early period of wound management.

Diagnostic value of procalcitonin, erythrocyte sedimentation rate (ESR), quantitative C-reactive protein (CRP) and clinical findings associated with osteomyelitis in patients with diabetic foot

2021 ◽  
pp. 1-7
Author(s):  
Zahra Soleimani ◽  
Fatemeh Amighi ◽  
Zarichehr Vakili ◽  
Mansooreh Momen-Heravi ◽  
Seyyed Alireza Moravveji
Keyword(s):  
Erythrocyte Sedimentation Rate ◽  
Sedimentation Rate ◽  
Roc Curve ◽  
Diabetic Foot ◽  
Diagnostic Value ◽  
Clinical Findings ◽  
Diabetic Patients ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Erythrocyte Sedimentation

BACKGROUND: The diagnosis of osteomyelitis is a key step of diabetic foot management. Procalcitonin (PCT) is a novel infection marker. This study aimed to investigate the diagnostic value of procalcitonin and other conventional infection markers and clinical findings in diagnosis of osteomyelitis in diabetic foot patients. METHODS AND MATERIALS: This diagnostic value study was carried out on ninety patients with diabetic infected foot ulcers admitted in Kashan Beheshti Hospital, 2016. After obtaining consent, 10 cc blood sample was taken for measuring serum PCT, CBC, ESR, CRP and FBS. Clinical characteristics of the wounds were noted. Magnetic resonance imaging of the foot was performed in all patients to diagnose osteomyelitis. All statistical analyses were done with the use of SPSS-16. RESULTS: PCT levels were 0.13 ± 0.02 ng/mili patients with osteomyelitis (n= 45) and 0.04 ± 0.02 ng/ml in patients without osteomyelitis (n= 45). PCT, Erythrocyte sedimentation rate and C-reactive protein was found significantly higher in patients with osteomyelitis (p< 0.001). The ROC curve was calculated for PCT. The area under the ROC curve for infection identification was 1 (p< 0.001). The best cut-off value for PCT was 0.085 ng/ml. Sensitivity, specificity, and positive and negative predictive values were 100%, 97.8%,97.8% and 100%, respectively. CONCLUSION: In this group of patients, PCT was useful to discriminate patients with bone infection. Also, Erythrocyte sedimentation rate and C-reactive protein can be used as a marker of osteomyelitis in diabetic patients.

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

A NOVEL EXTENSIVE EX-VIVO OCT DATABASE FROM MURINE MODELS OF COLORECTAL CANCER

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Bote ◽  
J F Ortega-Morán ◽  
C L Saratxaga ◽  
B Pagador ◽  
A Picón ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Structural Information ◽  
Ex Vivo ◽  
Ground Truth ◽  
Colon Polyps ◽  
Learning Methods ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
In Situ Methods

Abstract INTRODUCTION New non-invasive technologies for improving early diagnosis of colorectal cancer (CRC) are demanded by clinicians. Optical Coherence Tomography (OCT) provides sub-surface structural information and offers diagnosis capabilities of colon polyps, further improved by machine learning methods. Databases of OCT images are necessary to facilitate algorithms development and testing. MATERIALS AND METHODS A database has been acquired from rat colonic samples with a Thorlabs OCT system with 930nm centre wavelength that provides 1.2KHz A-scan rate, 7μm axial resolution in air, 4μm lateral resolution, 1.7mm imaging depth in air, 6mm x 6mm FOV, and 107dB sensitivity. The colon from anaesthetised animals has been excised and samples have been extracted and preserved for ex-vivo analysis with the OCT equipment. RESULTS This database consists of OCT 3D volumes (C-scans) and 2D images (B-scans) of murine samples from: 1) healthy tissue, for ground-truth comparison (18 samples; 66 C-scans; 17,478 B-scans); 2) hyperplastic polyps, obtained from an induced colorectal hyperplastic murine model (47 samples; 153 C-scans; 42,450 B-scans); 3) neoplastic polyps (adenomatous and adenocarcinomatous), obtained from clinically validated Pirc F344/NTac-Apcam1137 rat model (232 samples; 564 C-scans; 158,557 B-scans); and 4) unknown tissue (polyp adjacent, presumably healthy) (98 samples; 157 C-scans; 42,070 B-scans). CONCLUSIONS A novel extensive ex-vivo OCT database of murine CRC model has been obtained and will be openly published for the research community. It can be used for classification/segmentation machine learning methods, for correlation between OCT features and histopathological structures, and for developing new non-invasive in-situ methods of diagnosis of colorectal cancer.

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