Chronic wounds, which take over four weeks to heal, are a major global health issue linked to conditions such as diabetes, venous insufficiency, arterial diseases, and pressure ulcers. These wounds cause pain, reduce quality of life, and impose significant economic burdens. This systematic review explores the impact of technological advancements on the diagnosis of chronic wounds, focusing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. A literature search was conducted in databases including ACM, IEEE, PubMed, Scopus, and Web of Science, covering studies from 2013 to 2023. The focus was on articles applying complex computational techniques to analyze chronic wound images and clinical data. Exclusion criteria were non-image samples, review articles, and non-English or non-Spanish texts. From 2,791 articles identified, 93 full-text studies were selected for final analysis. The review identified significant advancements in tissue classification, wound measurement, segmentation, prediction of wound aetiology, risk indicators, and healing potential. The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care. The integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs. Continued research and innovation in computational techniques are essential to unlock their full potential in managing chronic wounds effectively.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Several studies have been conducted to analyze the role of social networks in communication between patients and health professionals, but there is a lack of studies on the uses made by health professions, through smartphones, only for professional matters. Objective: The aim of this study is to explore the social networking use accessed with smartphone applications, amongst primary care professions for only professional purposes. Methods: A cross-sectional study through an anonymous web survey was undertaken amongst 1,635 primary health care professionals. Results: 504 responses were recorded. Most of surveyed professionals habitually use Apps to access to social network sites (SNSs) with their smartphones (98.9%). WhatsApp is the preferred App (93.9%) for both personal and professional access. Most participants (75%) use the smartphone during their working time. The younger and with shorter working life respondents, had the greatest use of the Apps, privately and for professional purposes. The Apps with access to SNSs are used mostly to solve clinical problems (76.2%), improve professional knowledge (72%) and speed up the transmission of information (70%). Interprofessional communication (93.4%) and pharmacological consultations (56.4%) are the preferred uses, although small percentages use them to send or record clinical information (3.6%). Communication with patients through mobile Apps with access to SNSs still shows a reduced use (19%). Conclusions: Health professionals access to SNSs through their smartphones frequently and often for professional purposes. This trend suggests that they may be useful tools in primary care, but we must first ensure the security of the process and data if we want its use to be appropriate. Carrying out training activities by health institutions to make known what should be the correct use of these tools and inform about the most appropriate, could improve their use.

In this paper we analyze the characteristics of an experimental otolith identification system based on image contours described with Elliptical Fourier Descriptors (EFD). Otoliths are found in the inner ear of fishes. They are formed by calcium carbonate crystals and organic materials of proteic origin. Fish otolith shape analysis can be used for sex, age, population and species identification studies, and can provide necessary and relevant information for ecological studies. The system we propose has been tested for the identification of three different species, Engraulis encrasicholus, Pomadasys incisus belonging to the different families (Engroulidae and Haemolidae), and two populations of the species Merluccius merluccius (from CAT and GAL) from the family Merlucciidae. The identification of species from different families could be carried out quite easily with some simple class identifiers -i.e based on Support Vector Machine (SVM) with linear Kernel-; however, to identify these two populations that are characterized by a high similarity in their global form; a more accurate, and detailed shape representation of the otoliths are required, and at the same time the Otolith identifiers have to deal with a bigger number of descriptors. That is the principal reason that makes a challenging task both the design and the training of an otolith identification system, with a good performance on both cases.