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Analysis of the potential of innovation in dressings to treat chronic wounds in the city of Lima, Peru
(Universidad Ricardo Palma, Instituto de Investigaciones en Ciencias Biomedicas, Facultad de Medicina Humana, 2020-08-01)
Objective: To analyze the potential for innovation in dressings to treat chronic wounds in the City of Lima. Methods: A qualitative study was carried out by means of interviews to medical experts and purchasing managers of medical supplies for the treatment of difficult-to-resolve wounds in 8 representative public health institutions with categories 1-4 within the only 54 of Lima, Peru - 2018. Results: It was determined that an average of 17 patients is treated in public health institutions on a monthly basis (60% from hospitalization and 40% from an outpatient office). It is equivalent to say that 11,016 patients present chronic wounds of difficult resolution each year, which will require specialized treatment and an average annual demand of 110,160 dressings in stock. The dressings with the highest demand correspond to the Hydrogels and Hydrocolloids, respectively; used because of the positive results they offer in wound healing, despite economic limitations. The market price per unit ranges between 20 and 90 soles (S/.), representing an economic investment of 1500 soles on average per patient, in some cases causing complications or abandonment of treatment when resources are scarce. Conclusions: There is a high demand for patients with chronic wounds of difficult resolution in the public health institutions of Lima. It is important to promote and incentivize the investigation of new therapeutic alternatives and / or biomedical devices that favor its treatment.
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Regenerative medicine: the present's medicine
(Elsevier, 2020-09-09)
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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.
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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.
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Smartphone-Based 3D Thermal Monitoring of Chronic Wounds in a Clinical Setting
(RELX Group (Netherlands), 2023-01-01)
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