Hasil untuk "Dermatology"

T. Zuberbier, W. Aberer, R. Asero et al.

This evidence‐ and consensus‐based guideline was developed following the methods recommended by Cochrane and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group. The conference was held on 1 December 2016. It is a joint initiative of the Dermatology Section of the European Academy of Allergology and Clinical Immunology (EAACI), the EU‐founded network of excellence, the Global Allergy and Asthma European Network (GA²LEN), the European Dermatology Forum (EDF) and the World Allergy Organization (WAO) with the participation of 48 delegates of 42 national and international societies. This guideline was acknowledged and accepted by the European Union of Medical Specialists (UEMS). Urticaria is a frequent, mast cell‐driven disease, presenting with wheals, angioedema, or both. The lifetime prevalence for acute urticaria is approximately 20%. Chronic spontaneous urticaria and other chronic forms of urticaria are disabling, impair quality of life and affect performance at work and school. This guideline covers the definition and classification of urticaria, taking into account the recent progress in identifying its causes, eliciting factors and pathomechanisms. In addition, it outlines evidence‐based diagnostic and therapeutic approaches for the different subtypes of urticaria.

T. Zuberbier, A. A. Abdul Latiff, M. Abuzakouk et al.

This update and revision of the international guideline for urticaria was developed following the methods recommended by Cochrane and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group. It is a joint initiative of the Dermatology Section of the European Academy of Allergology and Clinical Immunology (EAACI), the Global Allergy and Asthma European Network (GA²LEN) and its Urticaria and Angioedema Centers of Reference and Excellence (UCAREs and ACAREs), the European Dermatology Forum (EDF; EuroGuiDerm), and the Asia Pacific Association of Allergy, Asthma and Clinical Immunology with the participation of 64 delegates of 50 national and international societies and from 31 countries. The consensus conference was held on 3 December 2020. This guideline was acknowledged and accepted by the European Union of Medical Specialists (UEMS). Urticaria is a frequent, mast cell–driven disease that presents with wheals, angioedema, or both. The lifetime prevalence for acute urticaria is approximately 20%. Chronic spontaneous or inducible urticaria is disabling, impairs quality of life, and affects performance at work and school. This updated version of the international guideline for urticaria covers the definition and classification of urticaria and outlines expert‐guided and evidence‐based diagnostic and therapeutic approaches for the different subtypes of urticaria.

Tobias Pincock

Wenbo Yang, Eman Rezk, Walaa M. Moursi et al.

Machine-learning models applied to skin images often have degraded performance when the skin colour captured in images (SCCI) differs between training and deployment. These discrepancies arise from a combination of entangled environmental factors (e.g., illumination, camera settings) and intrinsic factors (e.g., skin tone) that cannot be accurately described by a single "skin tone" scalar -- a simplification commonly adopted by prior work. To mitigate such colour mismatches, we propose a skin-colour disentangling framework that adapts disentanglement-by-compression to learn a structured, manipulable latent space for SCCI from unlabelled dermatology images. To prevent information leakage that hinders proper learning of dark colour features, we introduce a randomized, mostly monotonic decolourization mapping. To suppress unintended colour shifts of localized patterns (e.g., ink marks, scars) during colour manipulation, we further propose a geometry-aligned post-processing step. Together, these components enable faithful counterfactual editing and answering an essential question: "What would this skin condition look like under a different SCCI?", as well as direct colour transfer between images and controlled traversal along physically meaningful directions (e.g., blood perfusion, camera white balance), enabling educational visualization of skin conditions under varying SCCI. We demonstrate that dataset-level augmentation and colour normalization based on our framework achieve competitive lesion classification performance. Ultimately, our work promotes equitable diagnosis through creating diverse training datasets that include different skin tones and image-capturing conditions.

Ioana Cristina Alexandru, Mariana Grigore, Olga Simionescu

Cutaneous antigen-presenting cells (APCs), particularly dendritic cells (DCs) and Langerhans cells (LCs), are a diverse population of cells that play a vital role in immune surveillance by initiating and shaping skin immune responses. They link innate and adaptive immunity by presenting antigens, migrating, and activating T lymphocytes, thereby acting as orchestrators of tissue immunity. This review provides an updated overview of the morphofunctional diversity of cutaneous APCs, ranging from epidermal LCs and DCs, to dermal conventional DCs (DC1/DC2), plasmacytoid DCs (pDCs), including newly defined subsets such as DC3, Axl⁺Siglec-6⁺ DCs (ASDCs) and LAMP3⁺ mature regulatory DCs (mRegDCs). Dynamic differences in APC composition and function between homeostatic and inflamed skin are discussed, with particular emphasis on inflammatory and autoimmune conditions such as psoriasis, lupus erythematosus and chronic atopic dermatitis, in which distinct DC subsets contribute to Th1 and Th17 immune circuits. This review is the first skin-related approach that extensively discusses the cutaneous role of APCs in the neuro-immune-cutaneous axis, as well as their interactions with the local microenvironment. Ongoing controversies regarding the classification and stability of certain DC populations are discussed. A better understanding of the diversity, migration mechanisms and microenvironmental interactions of cutaneous APCs could lead to the identification of new biomarkers and therapeutic targets for inflammatory, autoimmune, and oncological skin diseases.

Phuong Daniels, DO, DPT, Lauren James, DO, Kaitlyn Stocks, BS et al.

Caroline Jacobzone-Lévêque, Rieko Tsubouchi, Rieko Tsubouchi et al.

Cutaneous atopy, which predominantly manifests as atopic dermatitis (AD), represents a significant global health concern due to its high prevalence and profound impact on patients’ quality of life. AD affects up to 20% of children and 10% of adults worldwide, with a rising incidence in both industrialized and developing regions. While genetic predisposition is a key determinant, most existing literature analyzes risk factors in isolation, limiting a comprehensive understanding of the disease. This article provides an integrative, regionally informed perspective on how environmental, cultural, genetic, and lifestyle factors may interact to shape the clinical expression of cutaneous atopy in diverse populations and regions. Drawing on published evidence and insights from an international expert panel, we highlight the complexity and multifactorial nature of the atopic environment, emphasizing the simultaneous disruption of the four key skin barriers—physical, chemical, immunological, and microbial—in AD. The article further addresses how regional differences in barrier function and the influence of urban versus rural living conditions can affect disease manifestation, underlining the need for personalized and holistic therapeutic strategies. This integrative perspective aims to offer healthcare professionals an updated framework for the management of AD, enabling more effective interventions tailored to the realities of diverse patient populations.

Kexin Yu, Zihan Xu, Jialei Xie et al.

Accurate diagnosis of skin diseases remains a significant challenge due to the complex and diverse visual features present in dermatoscopic images, often compounded by a lack of interpretability in existing purely visual diagnostic models. To address these limitations, this study introduces VL-MedGuide (Visual-Linguistic Medical Guide), a novel framework leveraging the powerful multi-modal understanding and reasoning capabilities of Visual-Language Large Models (LVLMs) for intelligent and inherently interpretable auxiliary diagnosis of skin conditions. VL-MedGuide operates in two interconnected stages: a Multi-modal Concept Perception Module, which identifies and linguistically describes dermatologically relevant visual features through sophisticated prompt engineering, and an Explainable Disease Reasoning Module, which integrates these concepts with raw visual information via Chain-of-Thought prompting to provide precise disease diagnoses alongside transparent rationales. Comprehensive experiments on the Derm7pt dataset demonstrate that VL-MedGuide achieves state-of-the-art performance in both disease diagnosis (83.55% BACC, 80.12% F1) and concept detection (76.10% BACC, 67.45% F1), surpassing existing baselines. Furthermore, human evaluations confirm the high clarity, completeness, and trustworthiness of its generated explanations, bridging the gap between AI performance and clinical utility by offering actionable, explainable insights for dermatological practice.

Fabian Gröger, Simone Lionetti, Philippe Gottfrois et al.

Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance. We introduce CleanPatrick, the first large-scale benchmark for data cleaning in the image domain, built upon the publicly available Fitzpatrick17k dermatology dataset. We collect 496,377 binary annotations from 933 medical crowd workers, identify off-topic samples (4%), near-duplicates (21%), and label errors (22%), and employ an aggregation model inspired by item-response theory followed by expert review to derive high-quality ground truth. CleanPatrick formalizes issue detection as a ranking task and adopts typical ranking metrics mirroring real audit workflows. Benchmarking classical anomaly detectors, perceptual hashing, SSIM, Confident Learning, NoiseRank, and SelfClean, we find that, on CleanPatrick, self-supervised representations excel at near-duplicate detection, classical methods achieve competitive off-topic detection under constrained review budgets, and label-error detection remains an open challenge for fine-grained medical classification. By releasing both the dataset and the evaluation framework, CleanPatrick enables a systematic comparison of image-cleaning strategies and paves the way for more reliable data-centric artificial intelligence.

Yang Zhou, Chrystie Wan Ning Quek, Jun Zhou et al.

Current artificial intelligence models for medical imaging are predominantly single modality and single disease. Attempts to create multimodal and multi-disease models have resulted in inconsistent clinical accuracy. Furthermore, training these models typically requires large, labour-intensive, well-labelled datasets. We developed MerMED-FM, a state-of-the-art multimodal, multi-specialty foundation model trained using self-supervised learning and a memory module. MerMED-FM was trained on 3.3 million medical images from over ten specialties and seven modalities, including computed tomography (CT), chest X-rays (CXR), ultrasound (US), pathology patches, color fundus photography (CFP), optical coherence tomography (OCT) and dermatology images. MerMED-FM was evaluated across multiple diseases and compared against existing foundational models. Strong performance was achieved across all modalities, with AUROCs of 0.988 (OCT); 0.982 (pathology); 0.951 (US); 0.943 (CT); 0.931 (skin); 0.894 (CFP); 0.858 (CXR). MerMED-FM has the potential to be a highly adaptable, versatile, cross-specialty foundation model that enables robust medical imaging interpretation across diverse medical disciplines.

Gernot Fiala, Markus Plass, Robert Harb et al.

A Whole Slide Image (WSI) is a high-resolution digital image created by scanning an entire glass slide containing a biological specimen, such as tissue sections or cell samples, at multiple magnifications. These images are digitally viewable, analyzable, and shareable, and are widely used for Artificial Intelligence (AI) algorithm development. WSIs play an important role in pathology for disease diagnosis and oncology for cancer research, but are also applied in neurology, veterinary medicine, hematology, microbiology, dermatology, pharmacology, toxicology, immunology, and forensic science. When assembling cohorts for AI training or validation, it is essential to know the content of a WSI. However, no standard currently exists for this metadata, and such a selection has largely relied on manual inspection, which is not suitable for large collections with millions of objects. We propose a general framework to generate 2D index maps (tissue maps) that describe the morphological content of WSIs using common syntax and semantics to achieve interoperability between catalogs. The tissue maps are structured in three layers: source, tissue type, and pathological alterations. Each layer assigns WSI segments to specific classes, providing AI-ready metadata. We demonstrate the advantages of this standard by applying AI-based metadata extraction from WSIs to generate tissue maps and integrating them into a WSI archive. This integration enhances search capabilities within WSI archives, thereby facilitating the accelerated assembly of high-quality, balanced, and more targeted datasets for AI training, validation, and cancer research.

Yupeng Zhang, Adam G. Dunn, Usman Naseem et al.

Medical artificial intelligence (AI) systems, particularly multimodal vision-language models (VLM), often exhibit intersectional biases where models are systematically less confident in diagnosing marginalised patient subgroups. Such bias can lead to higher rates of inaccurate and missed diagnoses due to demographically skewed data and divergent distributions of diagnostic certainty. Current fairness interventions frequently fail to address these gaps or compromise overall diagnostic performance to achieve statistical parity among the subgroups. In this study, we developed Cross-Modal Alignment Consistency (CMAC-MMD), a training framework that standardises diagnostic certainty across intersectional patient subgroups. Unlike traditional debiasing methods, this approach equalises the model's decision confidence without requiring sensitive demographic data during clinical inference. We evaluated this approach using 10,015 skin lesion images (HAM10000) with external validation on 12,000 images (BCN20000), and 10,000 fundus images for glaucoma detection (Harvard-FairVLMed), stratifying performance by intersectional age, gender, and race attributes. In the dermatology cohort, the proposed method reduced the overall intersectional missed diagnosis gap (difference in True Positive Rate, $Δ$TPR) from 0.50 to 0.26 while improving the overall Area Under the Curve (AUC) from 0.94 to 0.97 compared to standard training. Similarly, for glaucoma screening, the method reduced $Δ$TPR from 0.41 to 0.31, achieving a better AUC of 0.72 (vs. 0.71 baseline). This establishes a scalable framework for developing high-stakes clinical decision support systems that are both accurate and can perform equitably across diverse patient subgroups, ensuring reliable performance without increasing privacy risks.

Arthur Bouffandeau, Sabine Bensamoun, Robert Schleip et al.

Background: Palpation is the most widely used approach to empirically assess the mechanical properties of superficial tissues. While elastography is used for volume measurements, it remains difficult to assess skin properties with non-invasive methods. This study aimed to compare the performances of an impact-based analysis method (IBAM) consisting in studying the dynamic response of a punch in contact with the tissue with other approaches available on the market. Materials and Methods: IBAM consists in analyzing the time dependent force signal induced when a hammer instrumented with a force sensor impacts a cylindrical punch placed in contact with soft tissue. Sensitivities to stiffness changes and to spatial variations were compared between IBAM and four other mechanical surface characterization techniques: IndentoPro (macroindentation), Cutometer (suction), MyotonPro (damped oscillation) and Shore Durometer (durometry) using soft tissue phantoms based on polyurethane gel. Results: For stiffness discrimination in homogeneous phantoms, IBAM was slightly better than IndentoPro and MyotonPro (by 20 % and 35 % respectively), and outperformed the Shore Durometer and Cutometer by a factor of 2 to 4. Furthermore, for stiffness and thickness variations in bilayer phantoms, the axial sensitivity of IBAM was between 2.5 and 4.5 times better than that of MyotonPro and IndentoPro. In addition, the Cutometer appeared to be severely limited by its measurement depth. Conclusion: IBAM seems to be a promising technique for characterizing the mechanical properties of soft tissue phantoms at relatively low depth after future ex vivo and in vivo validation studies with biological tissues (with both animal and in human experiments). This work could pave the way to the development of a decision support system in the field of dermatology and cosmetics.

Lorraine Poncet, Noémie Roland, Romain Fortuna et al.

Summary: Background: A decrease in oral contraceptive use, newly available methods, and the emerging role of midwives suggested meaningful changes in contraception use in France. With two repeated cross-sectional studies, we aimed to describe contraception use in France in 2012 and 2022, in the total population and across age groups. Methods: Using the French National Health Data System (SNDS) covering 99% of the population, we identified reimbursed contraceptive use in women 15–49 years in January 2012 and 2022: combined oral contraceptives (COC), progestogen-only pill (POP), injectable progestogen, copper intrauterine device (Cu-IUD), levonorgestrel-releasing intrauterine devices (LNG-IUD), implants, sterilization. Number of users, socio-demographic characteristics and healthcare providers were assessed. Sales data accounted for non-reimbursed OC. Findings: Amid stable prevalence of contraception use (6.67 million users in 01/2012 and 6.73 in 01/2022, or 47%–46% of women aged 15–49 years), COC use decreased by a third from 54% (n = 3,602,803) to 35% of users (n = 2,370,205) while remaining the most popular method. POP and Cu-IUD use doubled, up to 19% (n = 1,293,073) and 21% of users (n = 1,428,837) users, respectively. IUD and POP have become leading methods in women 30–39 years, concerning 44% of users (n = 951,649) and 20% of users (n = 428,138) of users, respectively, while 50% of women ≥40 years (n = 1,051,066) used IUD. From <0.5% in 2012 (n = 16,154), midwives prescriptions reached 13% (n = 859,819) of total prescriptions in 2022. Social disparities in IUD use grew. Interpretation: Our findings displayed profound changes over ten years towards more hormone-free contraceptive methods. Funding: The French National Health Insurance Fund (Cnam) and the French National Agency for Medicines and Health Products Safety (ANSM) via the Scientific Interest Group EPI-PHARE.

Brandon R. Block, BA, Camille M. Powers, BS, Annie Chang, MS et al.

Raquel Lazcano, Daniel Madroñal, Giordana Florimbi et al.

Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks. To provide a complete study, two different medical applications, with two different requirements, have been analyzed. The first application consists of HS images taken from neurosurgical operations; the second one presents HS images taken from dermatological interventions. While the main constraint for neurosurgical applications is the processing time, in other environments, as the dermatological one, other requirements can be considered. In that sense, energy efficiency is becoming a major challenge, since this kind of applications are usually developed as hand-held devices, thus depending on the battery capacity. These requirements have been considered to choose the target platforms: on the one hand, three of the most powerful Graphic Processing Units (GPUs) available in the market; and, on the other hand, a low-power GPU and a manycore architecture, both specifically thought for being used in battery-dependent environments.

K. P. Santoso, R. V. H. Ginardi, R. A. Sastrowardoyo et al.

In the realm of skin lesion image classification, the intricate spatial and semantic features pose significant challenges for conventional Convolutional Neural Network (CNN)-based methodologies. These challenges are compounded by the imbalanced nature of skin lesion datasets, which hampers the ability of models to learn minority class features effectively. Despite augmentation strategies, such as those using Generative Adversarial Networks (GANs), previous attempts have not fully addressed these complexities. This study introduces an innovative approach by integrating Graph Neural Networks (GNNs) with Capsule Networks to enhance classification performance. GNNs, known for their proficiency in handling graph-structured data, offer an advanced mechanism for capturing complex patterns and relationships beyond the capabilities of traditional CNNs. Capsule Networks further contribute by providing superior recognition of spatial hierarchies within images. Our research focuses on evaluating and enhancing the Tiny Pyramid Vision GNN (Tiny Pyramid ViG) architecture by incorporating it with a Capsule Network. This hybrid model was applied to the MNIST:HAM10000 dataset, a comprehensive skin lesion dataset designed for benchmarking classification models. After 75 epochs of training, our model achieved a significant accuracy improvement, reaching 89.23% and 95.52%, surpassing established benchmarks such as GoogLeNet (83.94%), InceptionV3 (86.82%), MobileNet V3 (89.87%), EfficientNet-B7 (92.07%), ResNet18 (92.22%), ResNet34 (91.90%), ViT-Base (73.70%), and IRv2-SA (93.47%) on the same dataset. This outcome underscores the potential of our approach in overcoming the inherent challenges of skin lesion classification, contributing to the advancement of image-based diagnosis in dermatology.

Zhuoyi Fang, Jiajia Liu, Kexuan Shi et al.

Automatic melanoma segmentation is essential for early skin cancer detection, yet challenges arise from the heterogeneity of melanoma, as well as interfering factors like blurred boundaries, low contrast, and imaging artifacts. While numerous algorithms have been developed to address these issues, previous approaches have often overlooked the need to jointly capture spatial and sequential features within dermatological images. This limitation hampers segmentation accuracy, especially in cases with indistinct borders or structurally similar lesions. Additionally, previous models lacked both a global receptive field and high computational efficiency. In this work, we present the xLSTM-VMUNet Model, which jointly capture spatial and sequential features within dermatological images successfully. xLSTM-VMUNet can not only specialize in extracting spatial features from images, focusing on the structural characteristics of skin lesions, but also enhance contextual understanding, allowing more effective handling of complex medical image structures. Experiment results on the ISIC2018 dataset demonstrate that xLSTM-VMUNet outperforms VMUNet by 4.85% on DSC and 6.41% on IoU on the ISIC2017 dataset, by 1.25% on DSC and 2.07% on IoU on the ISIC2018 dataset, with faster convergence and consistently high segmentation performance. Our code is available at https://github.com/FangZhuoyi/XLSTM-VMUNet.