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.

A prospective, single-center, non-randomized, pre-marketing clinical investigation was conducted with a single group of subjects to collect skin lesion images. These images were subsequently utilized to compare the results obtained from a traditional method of wound size measurement with two novel methods developed using Machine Learning (ML) approaches. Both proposed methods automatically calculate the wound area from an image. One method employs a two-dimensional system with the assistance of an external calibrator, while the other utilizes an Augmented Reality (AR) system, eliminating the need for a physical calibration object. To validate the correlation between these methods, a gold standard measurement with digital planimetry was employed. A total of 67 wound images were obtained from 41 patients between 22 November 2022 and 10 February 2023. The conducted pre-marketing clinical investigation demonstrated that the ML algorithms are safe for both the intended user and the intended target population. They exhibit a high correlation with the gold standard method and are more accurate than traditional methods. Additionally, they meet the manufacturer’s expected use. The study validated the performance, safety, and usability of the implemented methods as a valuable tool in the measurement of skin lesions.

Purpose Chronic wounds, which take over six 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, focus- ing on how computational methods in wound image and data analysis improve diagnostic precision and patient outcomes. Methods 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 sam- ples, review articles, and non-English or non-Spanish texts. From 2711 articles identified, 93 full-text studies were selected for final analysis. Results From 2711 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 indica- tors, 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. Conclusions 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.

Hypergranulation in chronic wounds reflects impaired healing, leading to delayed recovery, increased risk of infection and higher treatment costs for healthcare systems. Despite its impact, hypergranulation is often misidentified in the early stages, hindering timely intervention. This study presents a deep learning‐based method to distinguish between hypergranulated and non‐hypergranulated tissue. The dataset comprised 6235 wound images from Hospital de la Santa Creu de Vic (Catalonia, Spain) and DFUC 2022, with balanced classes. Five architectures were evaluated using transfer learning: ViT, VGG16, RepVGG, MobileViT and RepGhost. RepGhost achieved the best results (81.4% accuracy, 89.4% area under the receiver operating characteristic curve [AUC]), outperforming both CNNs and transformers. Lightweight models (RepGhost, MobileViT) also demonstrated high performance, making them suitable for implementation on mobile devices. This tool may assist clinicians in the early detection of hypergranulation and improve wound treatment outcomes. This appears to be the first deep learning study on this condition to utilise a large, balanced dataset.

A solution is proposed that consists of supporting the professional in deciding how to act on the wound by offering a diagnosis proposal. Artificial Intelligence (AI) algorithms have been developed to allow the extraction of the most relevant wound characteristics through an image and providing similar successful wounds from the health center itself. Five pre-trained Convolutional Neural Networks (CNN) have been used to compare the results with images processed in different ways. In this way, the professional would have a diagnostic reference of other wounds similar to the one being evaluated and thus be able to make the right decision. A total of 711 images were processed and analyzed in order to obtain their most identifying morphological and textural characteristics. From each of the images, the five most similar images in terms of characteristics were searched for and clinically validated by comparing them using an objective assessment scale. The results showed an overall accuracy of 71.12%, calculated as the weighting of the scale match of similar images to the original. With this solution, clinicians improve their confidence in clinical practice by having support in decision making, observing favorable outcomes and progression of chronic wounds.