Computer-Assisted Differential Diagnosis of Pyoderma Gangrenosum and Venous Ulcers with Deep Neural Networks

Current landscape and future perspectives in the pathogenesis and treatment of Pyoderma gangrenosum

Pyoderma gangrenosum is a rare, ulcerative neutrophilic dermatosis of yet unknown origin. While advances have been made into the mechanisms underlying PG, our understanding is limited by a paucity of research and funding into this condition. This review highlights the current landscape of PG pathogenesis, diagnosis, and treatment in the hopes of encouraging additional scientific inquiry into this rare, severe condition.

Clinical Mimickers Misdiagnosed as Pyoderma Gangrenosum

Pyoderma gangrenosum (PG) is a rare, ulcerative, neutrophilic dermatosis that can be challenging to diagnose. Diagnosis of PG is clinical due to a lack of specific histopathologic, immunologic, or imaging findings associated with the disease, although several clinical frameworks exist to guide diagnosis. However, misdiagnosis of PG is frequent and leads to increased patient morbidity and mortality. This article highlights common mimickers of PG and offers clinical pearls to aid in accurate diagnosis with the goal of decreasing diagnostic delay and misdiagnosis and improving patient outcomes.

Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review

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.

Intelligent Diagnosis of Hypopigmented Dermatoses and Intelligent Evaluation of Vitiligo Severity on the Basis of Deep Learning

INTRODUCTION

There is a lack of objective, accurate, and convenient methods for classification diagnostic hypopigmented dermatoses (HD) and severity evaluation of vitiligo. To achieve an accurate and intelligent classification diagnostic model of HD and severity evaluation model of vitiligo using a deep learning-based method.

METHODS

A total of 11,483 images from 4744 patients with HD were included in this study. An optimal diagnostic model was constructed by merging the squeeze-and-excitation (SE) module with the candidate model, its diagnostic efficiency was compared with that of 98 dermatologists. An objective severity evaluation indicator was proposed through weighting method and combined with a segmentation model to form a severity evaluation model, which was then compared with the assessments conducted by three experienced dermatologists using the naked eye.

RESULTS

The improved diagnosis model SE_ResNet-18 outperformed the other 11 classic models with an accuracy of 0.9389, macro-specificity of 0.9878, and macro-f1 score of 0.9395, and outperformed the different categories of 98 dermatologists (P < 0.001). The weighted Kappa test indicated medium consistency between the Indicatorv and the VASIchange (K = 0.567, P < 0.05). The optimal segmented model, HR-Net, had 0.8421 mIOU. The model-based severity evaluation results were not significantly different among the three experienced dermatologists.

CONCLUSIONS

This study proposes an objective, accurate, and convenient hybrid model for diagnosing HD and evaluating the severity of vitiligo, providing a method for dermatologists especially in grassroots hospitals, and provides a foundation for telemedicine.

Approach to the Atypical Wound

The heterogeneity of atypical wounds can present diagnostic and therapeutic challenges; however, as the prevalence of atypical wounds grows worldwide, prompt and accurate management is increasingly an essential skill for dermatologists. Addressing the underlying cause of an atypical wound is critical for successful outcomes. An integrated approach with a focus on pain management and patient engagement is recommended to facilitate enduring wound closure. Advances in treatment, in addition to further research and clinical training, are necessary to address the expanding burden of atypical wounds.

Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review

OBJECTIVE

The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.

Narrative Review: Pyoderma Gangrenosum

Pyoderma gangrenosum (PG) is a skin lesion, characteristically a neutrophilic dermatosis, that can be complicated by rapid progression, necrosis, and ulceration. This is an important pathology to be discussed given that there are no established criteria for diagnosis or treatment. This review aims to elucidate characteristics and variations of PG that distinguish it from other ulcerative skin lesions. Variability in presentation can lead to missed or incorrect diagnosis, and some of the currently proposed criteria for categorizing and diagnosing PG have been included here. These criteria distinguish PG in terms of the nature of the lesion, the location, etiology, responsiveness to immunosuppressive therapy, and patient history. The etiology and pathogenesis of PG remain unknown, but we summarize prominent theories and explanations. Furthermore, recent research indicates that the incidence of PG has a strong correlation with autoimmune conditions, particularly inflammatory bowel disease. Major treatments for PG coincide with these findings, as the majority involve targeted anti-inflammatories, immunosuppressants, and surgical interventions. These treatments are addressed in this review, with added context for local versus systemic disease.