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Introduction: Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. Results: Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. Discussion: AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusions: AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems. [ABSTRACT FROM AUTHOR]

Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system's economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment starting from images acquired from smartphones. This method is based on an active semi-supervised learning training of a convolutional neural network model. In our work, we tested the robustness of our method against a wide range of natural images acquired in different light conditions and image expositions. We collected the images using an ad hoc developed app and saved them in a database which we then used for AI training. We then tested different CNN architectures to develop a balanced model, which we finally validated with a public dataset. We used a dataset of images acquired during clinical practice and built an annotated wound image dataset consisting of 1564 ulcer images from 474 patients. Only a small part of this large amount of data was manually annotated by experts (ground truth). A multi-step, active, semi-supervised training procedure was applied to improve the segmentation performances of the model. The developed training strategy mimics a continuous learning approach and provides a viable alternative for further medical applications. We tested the efficiency of our model against other public datasets, proving its robustness. The efficiency of the transfer learning showed that after less than 50 epochs, the model achieved a stable DSC that was greater than 0.95. The proposed active semi-supervised learning strategy could allow us to obtain an efficient segmentation method, thereby facilitating the work of the clinician by reducing their working times to achieve the measurements. Finally, the robustness of our pipeline confirms its possible usage in clinical practice as a reliable decision support system for clinicians. [ABSTRACT FROM AUTHOR]

Although nodular vasculitis (NV) and erythema induratum (Bazin's disease) have historically been considered synonymous, the terms erythema induratum and Bazin's disease are mainly used for cases of NV that are associated with I M i . I tuberculosis i infection, but the usage of the terms NV and erythema induratum still varies worldwide.1 Possible causes of NV include infections and neoplastic and autoimmune disorders, while drug-induced has been rarely reported. [Extracted from the article]

Introduction: Pyoderma gangrenosum (PG) poses a significant dermatological challenge due to its rapidly evolving painful necrotic ulcerations. Understanding its multifaceted pathogenesis and diverse clinical presentation is crucial for effective management., Objectives: We aimed to analyze demographic characteristics, clinical manifestations, lesion distributions, systemic disease associations, therapeutic interventions, and patient outcomes in PG cases., Methods: Medical records from 2017 to 2023 of PG patients at IRCCS Sant'Orsola Malpighi Hospital, Bologna, Italy, were retrospectively analyzed. Inclusion criteria encompassed persistent ulcers with clinical and histological evidence of PG, excluding cases with alternative diagnoses or inadequate follow-up. Clinical evaluations, including pain assessment and lesion measurements, were conducted at diagnosis and follow-up visits., Results: A total of 44 patients were evaluated. Pain was a universal symptom, and tissue pathergy was documented in 28.6% of patients. Ulcerative PG was the most common subtype (88.1%). Associations with inflammatory bowel diseases (25%), rheumatoid arthritis (9.1%), and hematological diseases (17.2%) were noted. Lower limbs were frequently affected (63.6%). Treatment approaches included wound management, topical and systemic corticosteroids, and immunosuppressive therapy, with varying response rates., Conclusion: Advanced dressing and steroid therapy were pivotal in mild PG cases, while moderate-to-severe cases often associated with systemic diseases showed incomplete healing despite treatment, especially in patients with inflammatory bowel diseases and hematological disorders. This study contributes to the understanding of PG's complexities, suggesting the use of biological therapy as first line in moderate-to-severe PG.