Hasil untuk "Dermatology"

Enno Schmidt, M. Sticherling, Miklós Sárdy et al.

(1) Department of Dermatology, University of Lübeck, Lübeck, Germany (2) Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany (3) Department of Dermatology, University Hospital Erlangen, Erlangen, Germany (4) Department of Dermatology and Venereology, University Hospital Munich (LMU), Munich, Germany (5) Department of Dermatology and Allergology, Philipps-Universität Marburg, Marburg, Germany (6) Department of Dermatology, Venereology, and Allergology, University Hospital Würzburg, Würzburg, Germany (7) Helios University Hospital Wuppertal, Department of Dermatology, Allergology and Dermatosurgery, Wuppertal, Germany (8) Department of Dermatology and Venereology, University of Cologne, Cologne, Germany (9) Department of Dermatology, Medical Center, University of Freiburg, Freiburg, Germany (10) Dermatologist in Private Practice, Fulda, Germany (11) Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Dermatology, Venereology und Allergy, Division of Evidence based Medicine (dEBM), Berlin, Germany (12) Gilead Sciences GmbH, Martinsried, Germany (13) Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institut für Klinische Pharmakologie und Toxikologie, Berlin, Germany (14) Department of Dermatology and Allergology, University Hospital Ulm, Ulm, Germany (15) Pediatric Immunology and Rheumatology, University Hospital and Outpatient Clinic for Pediatrics, Leipzig, Germany (16) Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Dermatology, Venereology und Allergy, Allergy Center, Berlin, Germany

F. Dalgard, U. Gieler, L. Tomas-Aragones et al.

The contribution of psychological disorders to the burden of skin disease has been poorly explored, and this is a large-scale study to ascertain the association between depression, anxiety, and suicidal ideation with various dermatological diagnoses. This international multicenter observational cross-sectional study was conducted in 13 European countries. In each dermatology clinic, 250 consecutive adult out-patients were recruited to complete a questionnaire, reporting socio-demographic information, negative life events, and suicidal ideation; depression and anxiety were assessed with the Hospital Anxiety and Depression Scale. A clinical examination was performed. A control group was recruited among hospital employees. There were 4,994 participants––3,635 patients and 1,359 controls. Clinical depression was present in 10.1% patients (controls 4.3%, odds ratio (OR) 2.40 (1.67–3.47)). Clinical anxiety was present in 17.2% (controls 11.1%, OR 2.18 (1.68–2.82)). Suicidal ideation was reported by 12.7% of all patients (controls 8.3%, OR 1.94 (1.33–2.82)). For individual diagnoses, only patients with psoriasis had significant association with suicidal ideation. The association with depression and anxiety was highest for patients with psoriasis, atopic dermatitis, hand eczema, and leg ulcers. These results identify a major additional burden of skin disease and have important clinical implications.

H. Duncan, D. OberG, W. Eyler

Hannah Verma, BA, Marguerite Meariman, MD, Benjamin Gerstein, BS et al.

Loris Cino, Pier Luigi Mazzeo, Alessandro Martella et al.

The integration of artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), into dermatological diagnosis demonstrates substantial clinical potential. While existing literature predominantly benchmarks algorithmic performance against human experts, our study adopts a novel perspective by investigating the intrinsic complexity of dermatoscopic images. Through rigorous experimentation with multiple CNN architectures, we isolated a subset of images systematically misclassified across all models-a phenomenon statistically proven to exceed random chance. To determine if these failures stem from algorithmic biases or inherent visual ambiguity, expert dermatologists independently evaluated these challenging cases alongside a control group. The results revealed a collapse in human diagnostic performance on the AI-misclassified images. First, agreement with ground-truth labels plummeted, with Cohen's kappa dropping to a mere 0.08 for the difficult images, compared to a 0.61 for the control group. Second, we observed a severe deterioration in expert consensus; inter-rater reliability among physicians fell from moderate concordance (Fleiss kappa = 0.456) on control images to only modest agreement (Fleiss kappa = 0.275) on difficult cases. We identified image quality as a primary driver of these dual systematic failures. To promote transparency and reproducibility, all data, code, and trained models have been made publicly available

Siyuan Wang, Ke Li, Jingyuan Huang et al.

Self-touch gestures (e.g., nuanced facial touches and subtle finger scratches) provide rich insights into human behaviors, from hygiene practices to health monitoring. However, existing approaches fall short in detecting such micro gestures due to their diverse movement patterns. This paper presents μTouch, a novel magnetic sensing platform for self-touch gesture recognition. μTouch features (1) a compact hardware design with low-power magnetometers and magnetic silicon, (2) a lightweight semi-supervised framework requiring minimal user data, and (3) an ambient field detection module to mitigate environmental interference. We evaluated μTouch in two representative applications in user studies with 11 and 12 participants. μTouch only requires three-second fine-tuning data for each gesture, and new users need less than one minute before starting to use the system. μTouch can distinguish eight different face-touching behaviors with an average accuracy of 93.41%, and reliably detect body-scratch behaviors with an average accuracy of 94.63%. μTouch demonstrates accurate and robust sensing performance even after a month, showcasing its potential as a practical tool for hygiene monitoring and dermatological health applications. Code is available at https://wangmerlyn.github.io/muTouch/.

Dimitrios P. Panagoulias, Evangelia-Aikaterini Tsichrintzi, Georgios Savvidis et al.

Human-in-the-loop validation is essential in safety-critical clinical AI, yet the transition between initial model inference and expert correction is rarely analyzed as a structured signal. We introduce a diagnostic alignment framework in which the AI-generated image based report is preserved as an immutable inference state and systematically compared with the physician-validated outcome. The inference pipeline integrates a vision-enabled large language model, BERT- based medical entity extraction, and a Sequential Language Model Inference (SLMI) step to enforce domain-consistent refinement prior to expert review. Evaluation on 21 dermatological cases (21 complete AI physician pairs) em- ployed a four-level concordance framework comprising exact primary match rate (PMR), semantic similarity-adjusted rate (AMR), cross-category alignment, and Comprehensive Concordance Rate (CCR). Exact agreement reached 71.4% and remained unchanged under semantic similarity (t = 0.60), while structured cross-category and differential overlap analysis yielded 100% comprehensive concordance (95% CI: [83.9%, 100%]). No cases demonstrated complete diagnostic divergence. These findings show that binary lexical evaluation substantially un- derestimates clinically meaningful alignment. Modeling expert validation as a structured transformation enables signal-aware quantification of correction dynamics and supports traceable, human aligned evaluation of image based clinical decision support systems.

M. A. Rasel, Sameem Abdul Kareem, Unaizah Obaidellah

Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typical) and irregular (atypical) PN is challenging. This study aims to automate the PN detection process using a directional imaging algorithm and classify PN types using machine learning classifiers. The directional imaging algorithm incorporates Principal Component Analysis (PCA), contrast enhancement, filtering, and noise reduction. Applied to the PH2 dataset, this algorithm achieved a 96% success rate, which increased to 100% after pixel intensity adjustments. We created a new dataset containing only PN images from these results. We then employed two classifiers, Convolutional Neural Network (CNN) and Bag of Features (BoF), to categorize PN into atypical and typical classes. Given the limited dataset of 200 images, a simple and effective CNN was designed, featuring two convolutional layers and two batch normalization layers. The proposed CNN achieved 90% accuracy, 90% sensitivity, and 89% specificity. When compared to state-of-the-art methods, our CNN demonstrated superior performance. Our study highlights the potential of the proposed CNN model for effective PN classification, suggesting future research should focus on expanding datasets and incorporating additional dermatological features to further enhance melanoma diagnosis.

Goel S., Malhan S., Kaur H. et al.

Urticaria is a severely pruritic and debilitating skin disorder having a profound impact on the quality of life. It can be classified as acute and chronic, which can be further classified as chronic spontaneous (CSU) and chronic inducible urticaria (CIndU). There is a notable scarcity of studies investigating the clinico-epidemiological profile of CSU and its impact on the quality of life. Objectives: This study was designed to investigate the clinico-epidemiological profile of patients with CSU, evaluate the severity of CSU using the urticaria activity score (UAS) and determine the impact of the illness on the quality of life using the dermatological quality of life index (DLQI).

B. Gonzalez Rodriguez, L. Calderon Lozano, J. Fulgencio Barbarin et al.

Guibin Gao, Juanjuan Li, Yanming Ma et al.

ABSTRACT Preserved/rescued mitochondrial functions have a significant effect on maintaining neurogenesis, axonal carriage, and synaptic plasticity following spinal cord injury (SCI). We fabricated an ingenious redox‐responsive strategy for commanded liberation of NADH (reduced form of nicotinamide‐adenine dinucleotide) by bioactive diselenide‐containing biodegradable mesoporous silica nanoparticles (Se@NADH). The nanocarrier‐embedded NADH can be liberated in a controlled pattern through the cleavage of diselenide bonds in the presence of reactive oxygen species (ROS) or glutathione (GSH). The NAD+ was regenerated by the reactions between released NADH and harmful ROS to antagonize mitochondrial dysfunction and increase ATP synthesis, promoting axon regeneration across SCI areas. This nanosystem increased the stability of NADH during prolonged blood circulation time, reduced the clearance rate, exhibited significant anti‐inflammatory as well as neuroprotective effects and enhanced the regeneration of electrophysiological conduction capacity across SCI areas. Importantly, Se@NADH suppressed glial scar formation and promoted neuronal generation as well as stretching of long axons throughout the glial scar, thereby improving actual restoration of locomotor functions in mice with SCI and exerting ascendant therapeutic effects. Targeting of mitochondrial dysfunction is a potential approach for SCI treatment and may be applied to other central nervous system diseases.

Federica Li Pomi, Andrea d’Aloja, Marta Vitale et al.

Abstract Introduction Actinic keratosis (AK) is recognized as the main precursor of cutaneous squamous cell carcinoma (cSCC). Given the unpredictable potential for progression, current guidelines recommend treating all AKs, irrespective of their clinical grade. However, many approved treatments are not indicated for hyperkeratotic AKs. Among topical therapies, 5-fluorouracil (5-FU) 4% cream (Tolak®/Tolerak®; Pierre Fabre) is a chemotherapeutic agent that has shown excellent results in treating non-hyperkeratotic AKs on the face, ear, and scalp, both in clinical trials and real-life experiences. However, its effectiveness in managing hyperkeratotic AKs remains unexplored. Methods A retrospective, single-center study was conducted at the Dermatology Unit of the University of Messina, Italy, between September 2024 and March 2025. The study included 66 hyperkeratotic AK lesions in 19 consecutive patients, treated with 5-FU cream for 28 consecutive days. Results At the 3-month follow-up, total clearance (complete lesion resolution) was observed in 54.5% of hyperkeratotic lesions, while partial clearance (> 75% lesion reduction) was recorded in 24.2%. The treatment demonstrated a good safety profile, with good patient tolerability. Among local skin reactions (LSRs), erythema was the most frequently observed, occurring in 89.5% of patients, followed by stinging, which was reported in 73.6% of cases. No patient discontinued the treatment as a result of the onset of adverse events. Conclusions Our findings, albeit initial, support the efficacy and safety of 5-FU 4% cream for the treatment of hyperkeratotic AKs.

Rifat Sadik, Tanvir Rahman, Arpan Bhattacharjee et al.

Deep learning models have shown remarkable success in dermatological image analysis, offering potential for automated skin disease diagnosis. Previously, convolutional neural network(CNN) based architectures have achieved immense popularity and success in computer vision (CV) based task like skin image recognition, generation and video analysis. But with the emergence of transformer based models, CV tasks are now are nowadays carrying out using these models. Vision Transformers (ViTs) is such a transformer-based models that have shown success in computer vision. It uses self-attention mechanisms to achieve state-of-the-art performance across various tasks. However, their reliance on global attention mechanisms makes them susceptible to adversarial perturbations. This paper aims to investigate the susceptibility of ViTs for medical images to adversarial watermarking-a method that adds so-called imperceptible perturbations in order to fool models. By generating adversarial watermarks through Projected Gradient Descent (PGD), we examine the transferability of such attacks to CNNs and analyze the performance defense mechanism -- adversarial training. Results indicate that while performance is not compromised for clean images, ViTs certainly become much more vulnerable to adversarial attacks: an accuracy drop of as low as 27.6%. Nevertheless, adversarial training raises it up to 90.0%.

Chengyu Yang, Rishik Reddy Yesgari, Chengjun Liu

Rosacea is a common but underdiagnosed inflammatory skin condition that primarily affects the central face and presents with subtle redness, pustules, and visible blood vessels. Automated detection remains challenging due to the diffuse nature of symptoms, the scarcity of labeled datasets, and privacy concerns associated with using identifiable facial images. A novel privacy-preserving automated rosacea detection method inspired by clinical priors and trained entirely on synthetic data is presented in this paper. Specifically, the proposed method, which leverages the observation that rosacea manifests predominantly through central facial erythema, first constructs a fixed redness-informed mask by selecting regions with consistently high red channel intensity across facial images. The mask thus is able to focus on diagnostically relevant areas such as the cheeks, nose, and forehead and exclude identity-revealing features. Second, the ResNet-18 deep learning method, which is trained on the masked synthetic images, achieves superior performance over the full-face baselines with notable gains in terms of accuracy, recall and F1 score when evaluated using the real-world test data. The experimental results demonstrate that the synthetic data and clinical priors can jointly enable accurate and ethical dermatological AI systems, especially for privacy sensitive applications in telemedicine and large-scale screening.

Chengyu Yang, Rishik Reddy Yesgari, Chengjun Liu

Rosacea, which is a chronic inflammatory skin condition that manifests with facial redness, papules, and visible blood vessels, often requirs precise and early detection for significantly improving treatment effectiveness. This paper presents new patch-based automatic rosacea detection strategies using the ResNet-18 deep learning framework. The contributions of the proposed strategies come from the following aspects. First, various image pateches are extracted from the facial images of people in different sizes, shapes, and locations. Second, a number of investigation studies are carried out to evaluate how the localized visual information influences the deep learing model performance. Third, thorough experiments are implemented to reveal that several patch-based automatic rosacea detection strategies achieve competitive or superior accuracy and sensitivity than the full-image based methods. And finally, the proposed patch-based strategies, which use only localized patches, inherently preserve patient privacy by excluding any identifiable facial features from the data. The experimental results indicate that the proposed patch-based strategies guide the deep learning model to focus on clinically relevant regions, enhance robustness and interpretability, and protect patient privacy. As a result, the proposed strategies offer practical insights for improving automated dermatological diagnostics.

M. A. Rasel, Sameem Abdul Kareem, Zhenli Kwan et al.

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm based on color threshold techniques on lesion patches and color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71% on the augmented PH2 dataset, 95.00% on the augmented ISIC archive dataset, 95.05% on the combined augmented (PH2+ISIC archive) dataset, and 90.00% on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process regarding BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Karishma Thakrar, Shreyas Basavatia, Akshay Daftardar

Dermatological care via telemedicine often lacks the rich context of in-person visits. Clinicians must make diagnoses based on a handful of images and brief descriptions, without the benefit of physical exams, second opinions, or reference materials. While many medical AI systems attempt to bridge these gaps with domain-specific fine-tuning, this work hypothesized that mimicking clinical reasoning processes could offer a more effective path forward. This study tested seven vision-language models on medical visual question answering across six configurations: baseline models, fine-tuned variants, and both augmented with either reasoning layers that combine multiple model perspectives, analogous to peer consultation, or retrieval-augmented generation that incorporates medical literature at inference time, serving a role similar to reference-checking. While fine-tuning degraded performance in four of seven models with an average 30% decrease, baseline models collapsed on test data. Clinical-inspired architectures, meanwhile, achieved up to 70% accuracy, maintaining performance on unseen data while generating explainable, literature-grounded outputs critical for clinical adoption. These findings demonstrate that medical AI succeeds by reconstructing the collaborative and evidence-based practices fundamental to clinical diagnosis.

Peng Huang, Junhu Fu, Bowen Guo et al.

As the appearance of medical images is influenced by multiple underlying factors, generative models require rich attribute information beyond labels to produce realistic and diverse images. For instance, generating an image of skin lesion with specific patterns demands descriptions that go beyond diagnosis, such as shape, size, texture, and color. However, such detailed descriptions are not always accessible. To address this, we explore a framework, termed Visual Attribute Prompts (VAP)-Diffusion, to leverage external knowledge from pre-trained Multi-modal Large Language Models (MLLMs) to improve the quality and diversity of medical image generation. First, to derive descriptions from MLLMs without hallucination, we design a series of prompts following Chain-of-Thoughts for common medical imaging tasks, including dermatologic, colorectal, and chest X-ray images. Generated descriptions are utilized during training and stored across different categories. During testing, descriptions are randomly retrieved from the corresponding category for inference. Moreover, to make the generator robust to unseen combination of descriptions at the test time, we propose a Prototype Condition Mechanism that restricts test embeddings to be similar to those from training. Experiments on three common types of medical imaging across four datasets verify the effectiveness of VAP-Diffusion.

Jhonatan Contreras, Thomas Bocklitz

Deep learning has achieved remarkable success in medical image analysis, however its adoption in clinical practice is limited by a lack of interpretability. These models often make correct predictions without explaining their reasoning. They may also rely on image regions unrelated to the disease or visual cues, such as annotations, that are not present in real-world conditions. This can reduce trust and increase the risk of misleading diagnoses. We introduce the Guided Focus via Segment-Wise Relevance Network (GFSR-Net), an approach designed to improve interpretability and reliability in medical imaging. GFSR-Net uses a small number of human annotations to approximate where a person would focus within an image intuitively, without requiring precise boundaries or exhaustive markings, making the process fast and practical. During training, the model learns to align its focus with these areas, progressively emphasizing features that carry diagnostic meaning. This guidance works across different types of natural and medical images, including chest X-rays, retinal scans, and dermatological images. Our experiments demonstrate that GFSR achieves comparable or superior accuracy while producing saliency maps that better reflect human expectations. This reduces the reliance on irrelevant patterns and increases confidence in automated diagnostic tools.

Bill Cassidy, Christian McBride, Connah Kendrick et al.

The growing rate of chronic wound occurrence, especially in patients with diabetes, has become a concerning trend in recent years. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Chronic wounds can have devastating consequences for the patient, with infection often leading to reduced quality of life and increased mortality risk. Innovative deep learning methods for the detection and monitoring of such wounds have the potential to reduce the impact to both patient and clinician. We present a novel multimodal segmentation method which allows for the introduction of patient metadata into the training workflow whereby the patient data are expressed as Gaussian random fields. Our results indicate that the proposed method improved performance when utilising multiple models, each trained on different metadata categories. Using the Diabetic Foot Ulcer Challenge 2022 test set, when compared to the baseline results (intersection over union = 0.4670, Dice similarity coefficient = 0.5908) we demonstrate improvements of +0.0220 and +0.0229 for intersection over union and Dice similarity coefficient respectively. This paper presents the first study to focus on integrating patient data into a chronic wound segmentation workflow. Our results show significant performance gains when training individual models using specific metadata categories, followed by average merging of prediction masks using distance transforms. All source code for this study is available at: https://github.com/mmu-dermatology-research/multimodal-grf