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 Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption.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.

Human Papillomavirus (HPV) infection poses a significant health risk, particularly for high-risk groups such as men who have sex with men (MSM), people living with HIV (PLHIV), and transgender individuals. Despite the availability of effective vaccines, uptake among these groups remains suboptimal due to various social and behavioral barriers (BeSD). A cross-sectional survey was conducted at the Sexually Transmitted Infections (STIs) clinic in Bologna, Italy, from 8 April to 12 April 2024 using a paper questionnaire, investigating HPV vaccine uptake and BeSD factors influencing vaccination decisions. Statistical analyses included descriptive statistics and multivariate logistic regression. Among the 236 respondents, PLHIV and transgender individuals demonstrated lower uptake rates (60.0% and 15.6%) if compared to women under 30 years old (72.7%). Concern about HPV infection varied significantly across groups, with MSM showing the highest worry (48.7%). Perceptions of vaccine safety and access were mixed, influencing vaccination decisions. Multivariate analysis indicated that age inversely correlated with infection worry (OR: 0.94, 95% CI: 0.91–0.98), while being a woman under 30 (OR: 164.0, 95% CI: 17.2–1560.18) or MSM (OR: 3.53, 95% CI: 1.37–9.11) was positively associated with vaccine uptake. The study identifies disparities in HPV vaccine uptake among STI clinic users in Bologna, Italy, emphasizing the need for targeted public health campaigns. These campaigns could engage STI clinics and address awareness, safety perceptions, and access barriers to enhance vaccination coverage among sexual and gender minorities.

Introduction. Early management of paediatric burn wounds is a critical determinant of wound progression, healing time, and long-term outcomes. The first 24 hours after injury represent a dynamic phase in which clinical decisions regarding cleansing, antisepsis, dressing selection, and follow-up may influence burn wound conversion and complications. Despite available guidelines, early paediatric burn care remains heterogeneous, and real-world data describing institutional practices are limited. Methods. A cross-sectional observational survey was conducted to describe early paediatric burn wound management practices in Italian burn centres. A structured, evidence-informed questionnaire was distributed electronically to designated clinical referents from 17 centres managing paediatric burns. Respondents were asked to report protocol-driven, centre-level practices regarding first aid, wound cleansing and antisepsis, initial dressing selection according to burn depth, timing of first reassessment, and caregiver education. Data were collected between May and June 2025 and analysed using descriptive statistics. Results. Eleven professionals participated, predominantly nurses (90.9%), most of whom worked in major burn centres. Cooling with running water for 20 minutes was the most frequently reported first-aid measure across burn aetiologies (90.9%). Wound cleansing was performed in all centres, most commonly using normal saline (63.6%). Antisepsis was frequently adopted, particularly for flame burns (100%), with 0.05% sodium hypochlorite being the most commonly reported agent. Paraffin-impregnated gauze was the preferred dressing for first-degree (81.8%) and superficial partial-thickness burns (72.7%). For deep partial-thickness and small full-thickness burns (<5 cm), collagenase, silver alginate, and hydrogel were more frequently selected. First wound reassessment occurred within 24 hours in 63.6% of centres. Most respondents (81.8%) reported providing structured caregiver education focused on home dressing management and pain control. Discussion and Conclusions. Early paediatric burn wound management practices in Italian burn centres appear broadly aligned with current first-aid and early reassessment recommendations. However, variability in antiseptic use and dressing selection persists, particularly for deeper burns, highlighting the need for shared, depth- and objective-driven frameworks to support early clinical decision-making and future multicentre consensus initiatives.

Wound edge assessment is a key component of chronic wound evaluation, but it remains highly subjective and affected by inter‐observer variability, particularly when performed on two‐dimensional clinical photographs. We retrospectively analysed 1 860 wound images acquired during routine clinical practice and independently annotated by four expert clinicians. An automated image‐analysis pipeline was used to segment the wound, standardise the peri‐wound border region, and estimate the three‐dimensional profile of the wound edge. We first tested whether geometry‐derived edge profiles alone could reproduce clinical wound edge categories. We then evaluated whether adding global visual descriptors of wound shape, colour appearance, and surface pattern improved agreement with clinicians. Inter‐clinician agreement was low, confirming the intrinsic subjectivity of wound edge classification. Geometry‐based analysis identified coherent edge‐profile patterns but showed poor correspondence with clinical annotations. In contrast, a supervised classifier incorporating both geometric and visual features achieved agreement comparable to, and in some comparisons higher than, the agreement observed among clinicians. Clinical wound edge assessment is not driven by edge geometry alone. Visual cues such as wound shape, colour appearance, and surface pattern appear to influence expert classification and may contribute to variability. Automated image‐based analysis may support more reproducible wound edge assessment, provided that it is externally validated in diverse clinical settings.

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 Scholas 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 limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusion: 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.