Skin Lesion Analysis Research Articles

Highly efferocytic M2 macrophages are generated at the termination stage of basophil-dependent skin allergy and dampen the inflammation

Basophils play critical roles in the development of delayed-onset skin allergic inflammation in mice (IgE-CAI model). Importantly, they also contribute to the resolution of allergic inflammation by promoting the generation of M2 macrophages. However, it remains unclear how M2 macrophages suppress excess inflammation. To address this, we conducted single-cell RNA-seq (scRNA-seq) analysis of the IgE-CAI skin lesion. scRNA-seq analysis identified two distinct M2 macrophage populations, namely early and late M2 macrophages, in IgE-CAI skin lesion. The former population was preferentially observed at the peak of inflammation (3 days after allergen challenge), whereas the latter one at the termination phase of inflammation (5 days). Gene ontology analysis revealed that genes associated with phagocytosis were enriched in late M2 macrophages. In particular, late M2 macrophages displayed upregulated expression of Gas6 and Mertk, key genes responsible for phagocytic clearance of apoptotic cells. Of note, MERTK inhibitor significantly aggravated ear swelling and accumulation of inflammatory cells in the skin lesion. Taken together, scRNA-seq identified M2 macrophages that display high capacity of dead cell clearance and contribute to the resolution of IgE-CAI.

Glomerular Kidney Diseases in the Single-Cell Era.

Recent advances in single-cell technology have enabled investigation of genomic profiles and molecular crosstalk among individual cells obtained from tissues and biofluids at unprecedented resolution. Glomerular diseases, either primary or secondary to systemic diseases, often manifest elements of inflammation and of innate and adaptive immune responses. Application of single-cell methods have revealed cellular signatures of inflammation, cellular injury, and fibrosis. From these signatures, potential therapeutic targets can be inferred and in theory, this approach might facilitate identification of precision therapeutics for these diseases. Single-cell analyses of urine samples and skin lesions from patients with lupus nephritis and of urine samples from patients with diabetic nephropathy and focal segmental glomerulosclerosis have presented potential novel approaches for the diagnosis and monitoring of disease activity. These single-cell approaches, in contrast to kidney biopsy, are non-invasive and could be repeated multiple times as needed.

Bio-P-10 - Evaluation of the role of different cell populations in mycosis fungoides microenvironment as a tool for biomarker identification for disease progression and individualized therapy

<h3>Introduction and objectives:</h3> Tumor microenvironment constitutes a promising area for the investigation of mechanisms involved in the development, progression, and drug responses in MF-CTCL. Mass cytometry (CyTOF) analysis gives a holistic view of different cell populations on skin tumor microenvironment by simultaneous identification and enumeration of over 37 immune cell subsets providing more information from other already established techniques. This study is focused on the evaluation of MF-CTCL patients' immune profile, in different types of skin lesions or sequential analysis of patients' skin lesions. <h3>Materials and methods:</h3> 14 MF-CTCL patients enrolled in this study. 4 patients with IA stage, 5 patients with IB stage, 5 patients IIB stage. Single cells were isolated from skin biopsies as follows: 2 samples from patch, 18 samples from plaques, and 7 samples from tumors. Sequential pre-, during and post-treatment biopsies were obtained from 3 patients, whereas biopsies before and after treatment were obtained from 2 patients. Finally, paired samples (plaque and tumors) were obtained from 5 patients. Five skin biopsies deriving from patients with non-malignant skin lesions were used as control. Single cells from 6mm punch biopsies were processed for CyTOF analysis using the Maxmar Direct Immune Profiling Assay for identification and enumeration of CD4+ and CD8+ (naive, central memory, effector memory, and terminal effector), CD4+ regulatory T cells, CD4–mucosal-associated invariant T cells (MAIT)/natural killer T (NKT) cells, B cell subsets (naive and memory, plasmablasts), natural killer (early and late) cells, T helper (Th) cell phenotypic subsets (Th1-like, Th2-like, and Th17-like), gamma delta (γδ) T cells, monocytes (classical, transitional, and nonclassical), dendritic cell subsets (plasmacytoid and myeloid), granulocytes, basophils, eosinophils, and neutrophils. We also determined the expression of 6 additional markers (PD-1, PD-L1, OX40, CD7, CD26, CD206). <h3>Results:</h3> We identified different cell subpopulations, either of malignant clones or immune cells of the microenvironment, providing evidence for the single-cell heterogeneity factors that are responsible for the heterogeneity in patients with MF at different disease stages. <h3>Conclusions:</h3> The heterogeneity that characterizes different stages of MF, the intra-individual and inter-individual differences, necessitates the application of this new "state of the art" technology (CyTOF) to provide holistic consideration of malignancy in correlation with the microenvironment, and it can be considered as a tool for precision and personalized therapeutic approach.

Image quality issues in teledermatology: A comparative analysis of artificial intelligence solutions

To the Editor: Deep learning artificial intelligence (AI) models capable of classifying skin lesions have become common in recent years. 1 Esteva A. Kuprel B. Novoa R.A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542: 115-118https://doi.org/10.1038/nature21056 Crossref PubMed Scopus (54) Google Scholar , 2 Codella N.C.F. Gutman D. Celebi M.E. et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018: 168-172 Crossref Scopus (407) Google Scholar , 3 Brinker T.J. Hekler A. Enk A.H. et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019; 113: 47-54https://doi.org/10.1016/j.ejca.2019.04.001 Abstract Full Text Full Text PDF PubMed Scopus (146) Google Scholar , 4 Lopez A.R. Giro-i-Nieto X. Burdick J. Marques O. Skin lesion classification from dermoscopic images using deep learning techniques. in: 2017 13th IASTED international conference on biomedical engineering (BioMed). IEEE, 2017: 49-54 Google Scholar Many models are built using high-quality professional images from publicly available datasets, introducing biases to the data. 5 Bissoto A. Valle E. Avila S. Debiasing skin lesion datasets and models? Not so fast. in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2020: 740-741 Google Scholar However, with the rise in teledermatology, patient-recorded images are often taken in poor, unnatural lighting, increasing the likelihood of inadequate contrast, color, or exposure, which presents a challenge to those models.

27403 Use of convolutional neural networks in skin lesion analysis using real world image and nonimage data

Introduction: Convolutional neural networks (CNNs) have demonstrated utility in high quality skin lesion image analysis. The use of CNNs in analyzing smartphone images needs further exploration to justify the development of real-time clinical decision support tools.

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models

Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples. These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploitable by the model. In the worst case, the trained model does not learn a valid and generalizable strategy to solve the problem it was trained for, and becomes a “Clever Hans” predictor that bases its decisions on spurious correlations in the training data, potentially yielding an unrepresentative or unfair, and possibly even hazardous predictor. In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora. Based on a recent technique — Spectral Relevance Analysis — we propose the following technical contributions and resulting findings: (a) a scalable quantification of artifactual and poisoned classes where the machine learning models under study exhibit Clever Hans behavior, (b) several approaches we collectively denote as Class Artifact Compensation, which are able to effectively and significantly reduce a model’s Clever Hans behavior, i.e., we are able to un-Hans models trained on (poisoned) datasets, such as the popular ImageNet data corpus. We demonstrate that Class Artifact Compensation, defined in a simple theoretical framework, may be implemented as part of a neural network’s training or fine-tuning process, or in a post-hoc manner by injecting additional layers, preventing any further propagation of undesired Clever Hans features, into the network architecture. Using our proposed methods, we provide qualitative and quantitative analyses of the biases and artifacts in, e.g., the ImageNet dataset, the Adience benchmark dataset of unfiltered faces, and the ISIC 2019 skin lesion analysis dataset. We demonstrate that these insights can give rise to improved, more representative, and fairer models operating on implicitly cleaned data corpora.

Dermoscopic image classification using CNN with Handcrafted features

Globally skin cancer is one of the main cause of death in humans. Early diagnosis plays a major role in increasing the prevention of death rate caused due to any kind of cancer. Conventional diagnosis of skin cancer is a tedious and time-consuming process. To overcome this an automated skin lesion classification must develop. Automated skin lesion classification is a challenging task due to the fine-grained variability in the visibility of skin lesions. In this work dermoscopic images are obtained from the International Skin Image Collaboration Archive 2016 (ISIC 2016). In the proposed method the analysis and classification of skin lesions is done with the help of a Convolution Neural Network (CNN) along with the hand crafted features of dermoscopic image using Scattered Wavelet Transform as additional input to the fully connected layer of CNN, which leads to an improvement in the accuracy for identifying Melanoma and different skin lesion classification when compared to the other state of the art methods. When raw dermoscopic image is given as an input to the CNN and feature values of segmented dermoscopic image as input to the fully connected layer as an additional information, the proposed method gives a classification accuracy of 98.13% for identification of Melanoma and the accuracy achieved for classification of skin lesions is 93.14% for Melanoma Vs Nevus, 95.4% for Seborrheic Keratosis (SK) vs Squamous Cell Carcinoma (SCC), 96.87% for Melanoma vs Seborrheic Keratosis (SK), 95.65% for Melanoma vs Basal Cell Carcinoma (BCC), 98.5% for Nevus vs Basal Cell Carcinoma(BCC).

Chitin-rich heteroglycan from Sporothrix schenckii sensu stricto potentiates fungal clearance in a mouse model of sporotrichosis and promotes macrophages phagocytosis

BackgroundFungal cell wall polysaccharides maintain the integrity of fungi and interact with host immune cells. The immunomodulation of fungal polysaccharides has been demonstrated in previous studies. However, the effect of chitin-rich heteroglycan extracted from Sporothrix schenckiisensu stricto on the immune response has not been investigated.ResultsIn this study, chitin-rich heteroglycan was extracted from S. schenckiisensu stricto, and immunomodulation was investigated via histopathological analysis of skin lesions in a mouse model of sporotrichosis and evaluation of the phagocytic function and cytokine secretion of macrophages in vitro. The results showed that the skin lesions regressed and granulomatous inflammation was reduced in infected mice within 5 weeks. Moreover, heteroglycan promoted the fungal phagocytosis by macrophages and modulated the cytokine secretion. Heteroglycan upregulated TNF-α expression early at 24 h and IL-12 expression late at 72 h after incubation, which might result from moderate activation of macrophages and contribute to the subsequent adaptive immune response.ConclusionsChitin-rich heteroglycan extracted from S. schenckiisensu stricto potentiated fungal clearance in a mouse model of sporotrichosis. Moreover, chitin-rich heteroglycan promoted fungus phagocytosis by macrophages and modulated cytokines secretion. These results might indicate that chitin-rich heteroglycan could be considered as an immunomodulator used in the treatment of sporotrichosis.

Dermo-DOCTOR: A framework for concurrent skin lesion detection and recognition using a deep convolutional neural network with end-to-end dual encoders

Background and objectiveAutomated skin lesion analysis for simultaneous detection and recognition is still challenging for inter-class homogeneity and intra-class heterogeneity, leading to low generic capability of a single convolutional neural network (CNN) with limited datasets. MethodsThis article proposes an end-to-end deep CNN-based framework for simultaneous detection and recognition of the skin lesions, named Dermo-DOCTOR, consisting of two encoders. The feature maps from two encoders are fused channel-wise, called Fused Feature Map (FFM). The FFM is utilized for decoding in the detection sub-network, concatenating each stage of two encoders’ outputs with corresponding decoder layers to retrieve the lost spatial information due to pooling in the encoders. For the recognition sub-network, the outputs of three fully connected layers, utilizing feature maps of two encoders and FFM, are aggregated to obtain a final lesion class. We train and evaluate the proposed Dermo-Doctor utilizing two publicly available benchmark datasets, such as ISIC-2016 and ISIC-2017. ResultsThe achieved segmentation results exhibit mean intersection over unions of 85.0% and 80.0% respectively for ISIC-2016 and ISIC-2017 test datasets. The proposed Dermo-DOCTOR also demonstrates praiseworthy success in lesion recognition, providing the areas under the receiver operating characteristic curves of 0.98 and 0.91 respectively for those two datasets. The experimental results show that the proposed Dermo-DOCTOR outperforms the alternative methods mentioned in the literature, designed for skin lesion detection and recognition. ConclusionAs the Dermo-DOCTOR provides better results on two different test datasets, even with limited training data, it can be an auspicious computer-aided assistive tool for dermatologists.

Artificial intelligence in dermatology and healthcare: An overview.

Many aspects of our life are affected by technology. One of the most discussed advancements of modern technologies is artificial intelligence. It involves computational methods which in some way mimic the human thought process. Just like other branches, the medical field also has come under the ambit of artificial intelligence. Almost every field in medicine has been touched by its effect in one way or the other. Prominent among them are medical diagnosis, medical statistics, robotics, and human biology. Medical imaging is one of the foremost specialties with artificial intelligence applications, wherein deep learning methods like artificial neural networks are commonly used. artificial intelligence application in dermatology was initially restricted to the analysis of melanoma and pigmentary skin lesions, has now expanded and covers many dermatoses. Though the applications of artificial intelligence are ever increasing, large data requirements, interpretation of data and ethical concerns are some of its limitations in the present day.

A mobile augmented reality application for supporting real-time skin lesion analysis based on deep learning

Melanoma is considered the deadliest skin cancer and when it is in an advanced state it is difficult to treat. Diagnoses are visually performed by dermatologists, by naked-eye observation. This paper proposes an augmented reality smartphone application for supporting the dermatologist in the real-time analysis of a skin lesion. The app augments the camera view with information related to the lesion features generally measured by the dermatologist for formulating the diagnosis. The lesion is also classified by a deep learning approach for identifying melanoma. The real-time process adopted for generating the augmented content is described. The real-time performances are also evaluated and a user study is also conducted. Results revealed that the real-time process may be entirely executed on the Smartphone and that the support provided is well judged by the target users.

Skin Lesion Segmentation Using Deep Learning with Auxiliary Task.

Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of , Accuracy (ACC) of , SEN of with only one integrated model, which can be learned in an end-to-end manner.

Proteomic analysis of psoriatic skin lesions in a Chinese population

Psoriasis is a chronic skin disorder with undefined pathogenesis. Several biomarkers for this disease have been identified by proteomic analysis. We explored the whole-proteomic changes in 45 pairs of psoriatic and adjacent noninvolved skin tissues in a Chinese population. A total of 3686 proteins were identified, of which 3008 were quantified. A total of 102 and 124 proteins were upregulated and downregulated in lesional skin, respectively. SART1 (P = 3.55 × 10−5) and GLTP (P = 1.54 × 10−3) were the most significantly down- and upregulated proteins. Nearly 90% of these differentially regulated proteins exhibited the same expression trends as those in an online RNA sequencing dataset for psoriasis; 19 differentially regulated proteins exhibited a negative relationship with DNA methylation data for psoriatic lesions. The differentially expressed proteins were enriched in ribosomes, antigen processing and presentation, immune response, and IL-17 signalling pathways. This study identified multiple differentially regulated proteins in psoriatic lesions, which suggested that changes in the proteome play important regulatory roles in psoriasis-associated processes. SignificanceProteomic analysis was performed in 45 pairs of psoriatic and adjacent noninvolved skin tissues in a Chinese population. More than 3000 proteins were quantified, of which 226 were differentially expressed in psoriatic skin tissues. These proteins were mainly enriched in the immune response, antigen processing and presentation and IL-17 signalling pathways, which have been reported to be associated with the pathogenesis of psoriasis.

In vivo diagnosis of skin cancer with a portable Raman spectroscopic device.

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10-fold cross-validation was performed. We obtained ROC AUCs of 0.75 (0.71-0.79; 95% CI), 0.69 (0.63-0.76; 95% CI) and 0.81 (0.74-0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy- and spectroscopy-based computer-assisted diagnosis systems to increase accuracy of neoplasms classification.

FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second, and great computational power to be trained. These factors make real-time semantic segmentation in low powered devices very hard. Therefore, in the present paper, we aim to modify particular aspects of the U-Net model to improve its performance through developing a fast U-Net (FU-Net) relying on bottleneck convolution layers in the contraction and expansion paths of the model. The proposed model can be utilized in semantic segmentation applications even on the devices with limited computational power and memory by ensuring the state-of-the-art performance. The amount of memory required by the proposed model is reduced by 23 times when compared with the original U-Net. Moreover, the modifications allowed achieving better performance. In conducted experiments, we assessed the performance of the proposed model on two biomedical image segmentation datasets, namely 2018 Data Science Bowl and ICIS 2018: Skin Lesion Analysis Towards Melanoma Detection. FU-Net demonstrated the state-of-the-art results in biomedical image segmentation, requiring the number of trainable parameters reduced by eight times compared with the original U-Net model. In addition, using bottleneck layers decreased the number of computations, resulting in nearly 30% speed-up at the training, validation and test stages. Furthermore, despite relying on fewer parameters FU-Net achieved a slight improvement of the performance in terms of pixel accuracy, Jaccard index, and dice coefficient evaluation metrics.

Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting

Due to the successful application of machine learning techniques in several fields, automated diagnosis system in healthcare has been increasing at a high rate. The aim of the study is to propose an automated skin cancer diagnosis and triaging model and to explore the impact of integrating the clinical features in the diagnosis and enhance the outcomes achieved by the literature study. We used an ensemble‐learning framework, consisting of the EfficientNetB3 deep learning model for skin lesion analysis and Extreme Gradient Boosting (XGB) for clinical data. The study used PAD‐UFES‐20 data set consisting of six unbalanced categories of skin cancer. To overcome the data imbalance, we used data augmentation. Experiments were conducted using skin lesion merely and the combination of skin lesion and clinical data. We found that integration of clinical data with skin lesions enhances automated diagnosis accuracy. Moreover, the proposed model outperformed the results achieved by the previous study for the PAD‐UFES‐20 data set with an accuracy of 0.78, precision of 0.89, recall of 0.86, and F1 of 0.88. In conclusion, the study provides an improved automated diagnosis system to aid the healthcare professional and patients for skin cancer diagnosis and remote triaging.

Robust Skin Disease Classification by Distilling Deep Neural Network Ensemble for the Mobile Diagnosis of Herpes Zoster

Herpes zoster (HZ) is a common cutaneous disease affecting one out of five people; hence, early diagnosis of HZ is crucial as it can progress to chronic pain syndrome if antiviral treatment is not provided within 72 hr. Mobile diagnosis of HZ with the assistance of artificial intelligence can prevent neuropathic pain while reducing clinicians’ fatigue and diagnosis cost. However, the clinical images captured from daily mobile devices likely contain visual corruptions, such as motion blur and noise, which can easily mislead the automated system. Hence, this paper aims to train a robust and mobile deep neural network (DNN) that can distinguish HZ from other skin diseases using user-submitted images. To enhance robustness while retaining low computational cost, we propose a knowledge distillation from ensemble via curriculum training (KDE-CT) wherein a student network learns from a stronger teacher network progressively. We established skin diseases dataset for HZ diagnosis and evaluated the robustness against 75 types of corruption. A total of 13 different DNNs was evaluated on both clean and corrupted images. The experiment result shows that the proposed KDE-CT significantly improves corruption robustness when compared with other methods. Our trained MobileNetV3-Small achieved more robust performance (93.5% overall accuracy, 67.6 mean corruption error) than the DNN ensemble with smaller computation (549x fewer multiply-and-accumulate operations), which makes it suitable for mobile skin lesion analysis.

Cascade knowledge diffusion network for skin lesion diagnosis and segmentation

Accurate diagnosis and segmentation of skin lesion is critical for early detection and diagnosis of skin cancer. Recent multi-task learning methods require expensive annotations for skin lesion analysis while single-task driven models cannot fully utilize the potential knowledge. The aim of this study is to utilize the neglected knowledge by a flexible architecture in dermoscopy skin lesion classification and segmentation. In this work, we propose a cascade knowledge diffusion network (CKDNet) to transfer and aggregate knowledge learnt from different tasks to simultaneously boost the performances of classification and segmentation. CKDNet consists of a sequence of coarse-level segmentation, classification, and fine-level segmentation networks. We design two novel feature entanglement modules, Entangle-Cls and Entangle-Seg, for classification and segmentation. The Entangle-Cls module aggregates the diffused features from initial segmentation to drive the classification network’s attention to image regions relevant to the disease. The Entangle-Seg module integrates the cascaded context knowledge learnt from classification to benefit fine-level segmentation, especially at uncertain boundaries. The entanglement modules can adaptively control the knowledge that can be diffused from one task to another, which avoids the empirical selection of weights for different learning tasks compared to other multi-task methods. We perform extensive evaluations and comparisons with state-of-the-art methods on skin lesion classification and segmentation with challenge datasets, ISIC2017 and ISIC2018. Our CKDNet demonstrated superior performance without using any ensemble approaches or any external datasets. The effectiveness of each component and loss functions are demonstrated by interpretable results using class activation maps (CAM), t-SNE, and classification and segmentation results.

Digital future of dermatology

Evolution of technology in the past several decades has undeniably transformed the practice of medicine. Dermatology, a field relying on visual cues, has been particularly impacted by advancement in imaging technologies. The purpose of this study was to review the current status as well as digital future of dermatology. The PubMed database was searched for articles pertaining to digital dermatology using search terms digital dermatology, teledermatology, and dermatopathology education. Digital dermatology has found a role in almost every aspect of dermatology: research, dermatology education and training, and clinical practice including disease prevention, diagnosis, treatment, and patient follow-up. Smartphone applications such VisualDx, MyDermPath, YouDermoscopy serve as diagnostic aid tools and can also help increase the user's knowledge of dermatology. Tools such as multispectral digital skin lesion analysis (MSDSLA) improve diagnostic accuracy and lead to fewer unnecessary biopsies. Teledermatology increases patient satisfaction, as they are able to experience shorter waits times and decreased costs. Underserved communities and those in rural settings are more likely to have a dermatologic evaluation by a specialist via teledermatology. Addressing important topics such as legal framework and updating reimbursement policies will allow for a smoother incorporation of digital dermatology into clinical practice and likely benefit patient care.

Analysis of pigmented skin lesions and malignant melanoma by Laser Doppler flowmetry - Report of different cases and further analysis by a neuronal network.

Dermatoscopy is successfully used for the early diagnosis of suspicious skin lesions, however, correct diagnosis depends on training. There is evidence that wavelet analysis by Laser Doppler flowmetry (LDF) can identify malignant melanomas by their hypervascularization and changes in the capillary morphology. To show the capability of LDF in the distinction of melanomas and benign pigmented skin lesions based on data collected over 16 years. Evaluation of pigmented skin lesions was based on clinical information. The LDF measurements were taken. The suspect lesion was excised afterwards for histological work-up. Four case reports are presented. Data collected over 16 years was processed into a neuronal network to estimate the dignity of the lesion. A total of 517 suspicious lesions were analyzed by LDF. In the histological work-up, 114 lesions turned out to be melanomas, whereas 403 benign naevi were secured. Specificity to detect melanomas was good based on the clinical information. The LDF increases the sensitivity of melanoma detection, which is also illustrated in four case reports. In addition to clinical parameters, such as color and border, information from the LDF can help in the diagnosis of malignant melanomas. The LDF provides information on the vascularization of the skin lesion.