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Item type:Publication, Diabetes Status is Associated With Plantar Soft Tissue Stiffness Measured Using Ultrasound Reverberant Shear Wave Elastography Approach(SAGE Publishing, 2020-10-23)Introduction: The purpose of this study was to investigate the association between the mechanical properties of plantar soft tissue and diabetes status. Method: 51 (M/F: 21/30) participants with prediabetes onset (fasting blood sugar [FBS] level > 100 mg/dL), age >18 years, and no lower limb amputation were recruited after ethical approval was granted from Pontificia Universidad Catolica del Peru ethical review board. Ultrasound reverberant shear wave elastography was used to assess the soft tissue stiffness at the 1st metatarsal head (MTH), 3rd MTH, and the heel at both feet. Results: Spearman’s rank-order correlation (rho) test indicated a significant ( P < .05) positive correlations between FBS level and the plantar soft tissue shear wave speed at the 1st MTH: rho = 0.402 (@400 Hz), rho = 0.373 (@450 Hz), rho = 0.474 (@500 Hz), rho= 0.395 (@550 Hz), and rho = 0.326 (@600 Hz) in the left foot and rho = 0.364 (@450 Hz) in the right foot. Mann-Whitney U test indicated a significantly ( P < .05) higher shear wave speed in the plantar soft tissue with the following effect sizes (r) at the 1st MTH of the left foot at all tested frequencies: r = 0.297 (@450 Hz), r = 0.345 (@500 Hz), r = 0.322 (@550 Hz), and r = 0.275 (@600 Hz), and at the 1st MTH of right foot r = 0.286 (@400 Hz) in diabetes as compared with the age and body mass index matched prediabetes group. Conclusion: An association between fasting blood sugar level and the stiffness of the plantar soft tissue with higher values of shear wave speed in diabetes versus prediabetes group was observed. This indicated that the proposed approach can improve the assessment of the severity of diabetic foot complications with potential implications in patient stratification. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multimodal and multiview wound monitoring with mobile devices(MDPI, 2021-10-01)Along with geometric and color indicators, thermography is another valuable source of information for wound monitoring. The interaction of geometry with thermography can provide predictive indicators of wound evolution; however, existing processes are focused on the use of high-cost devices with a static configuration, which restricts the scanning of large surfaces. In this study, we propose the use of commercial devices, such as mobile devices and portable thermography, to integrate information from different wavelengths onto the surface of a 3D model. A handheld acquisition is proposed in which color images are used to create a 3D model by using Structure from Motion (SfM), and thermography is incorporated into the 3D surface through a pose estimation refinement based on optimizing the temperature correlation between multiple views. Thermal and color 3D models were successfully created for six patients with multiple views from a low-cost commercial device. The results show the successful application of the proposed methodology where thermal mapping on 3D models is not limited in the scanning area and can provide consistent information between multiple thermal camera views. Further work will focus on studying the quantitative metrics obtained by the multi-view 3D models created with the proposed methodology. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Plantar soft tissue characterization using reverberant shear wave elastography: a proof-of-concept study(Elsevier, 2021-10-25)Plantar soft tissue stiffness provides relevant information on biomechanical characteristics of the foot. Therefore, appropriate monitoring of foot elasticity could be useful for diagnosis, treatment or health care of people with complex pathologies such as a diabetic foot. In this work, the reliability of reverberant shear wave elastography (RSWE) applied to plantar soft tissue was investigated. Shear wave speed (SWS) measurements were estimated at the plantar soft tissue at the first metatarsal head, the third metatarsal head and the heel from both feet in five healthy volunteers. Experiments were repeated for a test–retest analysis with and without the use of gel pad using a mechanical excitation frequency range between 400 and 600 Hz. Statistical analysis was performed to evaluate the reliability of the SWS estimations. In addition, the results were compared against those obtained with a commercially available shear wave-based elastography technique, supersonic imaging (SSI). The results indicate a low coefficient of variation for test–retest experiments with gel pad (median: 5.59%) and without gel pad (median: 5.83%). Additionally, the values of the SWS measurements increase at higher frequencies (median values: 2.11 m/s at 400 Hz, 2.16 m/s at 450 Hz, 2.24 m/s at 500 Hz, 2.21 m/s at 550 Hz and 2.31 m/s at 600 Hz), consistent with previous reports at lower frequencies. The SWSs at the plantar soft tissue at the first metatarsal head, third metatarsal head and heel were found be significantly (p<0.05) different, with median values of 2.42, 2.16 and 2.03 m/s, respectively which indicates the ability of the method to differentiate between shear wave speeds at different anatomical locations. The results indicated better elastographic signal-to-noise ratios with RSWE compared to SSI because of the artifacts presented in the SWS generation. These preliminary results indicate that the RSWE approach can be used to estimate the plantar soft tissue elasticity, which may have great potential to better evaluate changes in biomechanical characteristics of the foot. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multi-view data augmentation to improve wound segmentation on 3D surface model by deep learning(Institute of Electrical and Electronics Engineers Inc., 2021-01-01)Wound area segmentation really progressed with the emergence of deep learning, due to its robustness in uncontrolled lighting and no need to design hand-crafted features but two limits have still to be overcome: firstly, its performance relies on the size and quality of the training dataset in the medical field, where data annotation is costly and time-consuming; secondly the accuracy of the segmentation depends highly on the camera distance and angle and moreover perspective effects prevent measuring real surfaces in single views. To address concurrently these two issues, we propose to apply multi-view modeling: an image sequence is acquired around the wound site and enables wound 3D reconstruction. Then, a segmentation step is run to extract roughly the wound from the background in each view and to select the best view with an original strategy. This view provides the most accurate segmentation and the real wound bed area even on non planar wounds. Finally, this segmentation is backprojected in each view to generate a complete set of well annotated real images to reinforce the learning step of the neural network. In our experiments, we compare several strategies to select the best view in the image sequence. The proposed method, tested on a dataset of 270 images, outperforms standard deep learning approach based on a single view, as recorded with DICE index and IoU score which rise respectively from 36.53% to 86.3% and 29.48% to 77.09% for the wound class to achieve an overall DICE and IoU score of 93.04% and 86.61% including background class. These results attest to the robustness of our method and its improved accuracy in the wound segmentation task. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Smartphone-Based 3D Thermal Monitoring of Chronic Wounds in a Clinical Setting(RELX Group (Netherlands), 2023-01-01)