Real-life practice of reflectance confocal microscopy in France: A prospective multicenter study.

BACKGROUND: Studies demonstrating the potential utility of reflectance confocal microscopy (RCM) have been performed under experimental conditions. OBJECTIVE: To provide an overview of RCM practice in real-life. METHODS: A multicenter, prospective study carried out in 10 university dermatology departments in France. RESULTS: Overall, 410 patients were enrolled. One-half of the patients (48%) were referred by private practice dermatologists. They were referred for diagnosis (84.9%) or presurgical mapping (13%). For diagnosis, the lesions were located on the face (62%), arms and legs (14.9%), and trunk (13.6%), and presurgical mapping was almost exclusively on the face (90.9%). Among those referred for diagnosis, the main indication was suspicion of a skin tumor (92.8%). Of these, 50.6% were spared biopsies after RCM. When RCM indicated surgery, histology revealed malignant lesions in 72.7% of cases. The correlation between RCM and histopathology was high, with a correlation rate of 82.76% and a kappa coefficient of 0.73 (0.63; 0.82). LIMITATIONS: This study was performed in the settings of French tertiary referral hospitals. CONCLUSION: This study shows that in real-life RCM can be integrated into the workflow of a public private network, which enables a less invasive diagnostic procedure for patients.

Evaluating medical undergraduates' and dermatology faculty members' perception of using three-dimensional images as part of dermatology education in a time of COVID-19: a mixed-methods study.

BACKGROUND: The coronavirus-19 pandemic has impacted the delivery of medical education in dermatology, leading to decreased patient contact. There arose a need to pioneer innovative teaching tools to augment current methods for now and beyond the pandemic. OBJECTIVES: We aimed to assess the utility of three-dimensional (3D) images in the learning and teaching of dermatology by analysing the perceptions of medical undergraduates and faculty members in a qualitative and quantitative study. METHODS: Medical undergraduates (n = 119) and dermatology faculty members (n = 20) were recruited on a voluntary basis to watch a showcase session using a portable 3D imaging system allowing 3D images of skin lesions to be examined and digitally manipulated. After the session, participants filled in an anonymous questionnaire evaluating their perceptions. RESULTS: Of the 119 learners, most (> 84%) strongly agreed/agreed that (i) they would have more confidence in the field of dermatology; (ii) their ability to describe skin lesions would increase; (iii) their understanding of common dermatological conditions would increase; (iv) 3D images allow a greater approximation to real-life encounters than 2D images; and (v) learning with this modality would be useful. Of the 20 faculty members, most (> 84%) strongly agreed/agreed that (i) it is easier to teach with the aid of 3D images, and (ii) they would want access to 3D images during teaching sessions. Skin tumours were perceived to be learnt best via this modality in terms of showcasing topography (P < 0.01) and close approximation to real-life (P < 0.001). Overall, thematic analysis from qualitative analysis revealed that conditions learnt better with 3D images were those with surface changes and characteristic topography. CONCLUSIONS: Our results show that the greatest utility of 3D images lies in conditions where lesions have skin surface changes in the form of protrusions or depressions, such as in skin tumours or ulcers. As such, 3D images can be useful teaching tools in dermatology, especially in conditions where appreciation of surface changes and topography is important.

Patient-led teledermatology for skin lesion triage: a service evaluation of the DyplensTM dermoscope.

Despite the huge improvement in smartphone cameras, there has not been any real interest in the UK in pursuing patient-facing teledermatology within the sphere of skin lesion triage. High-specification dermoscopic images can be generated with smartphone attachments, but, to date, no formal clinical trial has been performed to establish the efficacy and feasibility of these consumer-level dermoscopes in skin lesion triage. The objectives of this study were to assess the ability of patients to capture dermoscopic images using a smartphone attachment, and to identify the safety and diagnostic accuracy of consumer-level dermoscopy in triaging out benign skin lesions from the 2-week-wait (2WW) cancer pathway. We recruited 78 patients already attending a face-to-face clinic at two locations. They were provided with instruction leaflets and asked to obtain dermoscopic and macroscopic images of their lesion(s) using their own smartphones. The images (and a brief history) were distributed to five experienced blinded assessors (consultants), who were asked to state their working diagnosis and outcome (reassurance, routine review or 2WW pathway), as they would in teledermatology. We compared their outcomes to the gold-standard in-person diagnosis and/or histological diagnosis, where available. The device achieved 100% sensitivity in diagnosing melanoma and squamous cell carcinoma (SCC). The specificity for the diagnoses of melanoma (89%) and SCC (83%) was high. The overall diagnostic accuracy was 77% for both benign and malignant lesions, The diagnostic accuracy was high for seborrhoeic keratosis (91%) and simple naevi (81%). Patient-captured dermoscopic images using bespoke smartphone attachments could be the future in safely triaging out benign lesions.

Seeing beyond skin deep: High-resolution ultrasound in dermatology-A comprehensive review and future prospects.

High-resolution ultrasound (HRUS), operating at frequencies of 20-25 MHz, is a non-invasive imaging tool that offers dermatologists the ability to visualize structures beneath the skin surface. The objective of this review is to present a comprehensive overview of HRUS applications, emphasising its utility in diagnosing, characterising and managing various dermatological conditions. We undertook a comprehensive literature review on the dermatological application of HRUS across Medline, Embase and Cochrane Library databases, while also incorporating our own clinical experience of over 16 years with the tool. In normal skin, the epidermis and dermis are hyperechoic, and the subcutaneous layer is hypoechoic. Basal cell carcinomas appear hypoechoic with irregular margins, while the presence of hyperechoic inclusion bodies suggests aggressive pathology. Squamous cell carcinomas pose challenges due to acoustic shadow artefacts from the thickened stratum corneum. Melanomas are homogenous hypoechoic lesions, with HRUS used to accurately predict Breslow thickness. HRUS provides dermatologists with a valuable adjunct to traditional clinical examination. Future advancement in image resolution and the standardisation of diagnostic parameters may further expand its utility.

Features of tattoo-associated cutaneous lymphoid hyperplasia on reflectance confocal microscopy and line-field confocal optical coherence tomography.

The popularity of tattoos has led to an increase in associated skin reactions, including complications such as infection, allergic reactions and rare conditions such as tattoo-induced cutaneous lymphoid hyperplasia (CLH). CLH is a benign lymphoproliferative reaction with clinical features resembling malignant cutaneous lymphomas. Non-invasive diagnostic tools like reflectance confocal microscopy (RCM) and the new line-field confocal optical coherence tomography (LC-OCT) are being studied in dermatology better to understand the morphological patterns of many dermatological diseases. Between September 2021 and May 2023, patients with suspicious lesions for tattoo-related CLH were analysed using RCM and LC-OCT before confirming the diagnosis of CLH through skin biopsy and histopathological examination. The study included five cases of CLH. It focused on the analysis of high-quality LC-OCT images/videos and RCM images to investigate the features of CLH in tattooed individuals. Most (80%) cases exhibited a mixed T and B lymphocyte infiltration subtype, while 20% showed a predominant T infiltration subtype. RCM and LC-OCT revealed characteristic features, including architectural disarray, fibrosis, lymphoid infiltrates, and pigment deposits in the epidermis and dermis. Non-invasive tools such as RCM and LC-OCT are valuable in diagnosing tattoo-related CLH. While skin biopsy remains the current standard for diagnosis, RCM and LC-OCT can serve as helpful adjuncts in identifying the most representative area for biopsy. They may potentially become alternative diagnostic options in the future, offering benefits in terms of cost, diagnostic efficiency, aesthetics and patient satisfaction as the prevalence of tattoo-related adverse reactions continues to rise.

Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis: Complementary information that may affect decision-making.

In dermatology, deep learning may be applied for skin lesion classification. However, for a given input image, a neural network only outputs a label, obtained using the class probabilities, which do not model uncertainty. Our group developed a novel method to quantify uncertainty in stochastic neural networks. In this study, we aimed to train such network for skin lesion classification and evaluate its diagnostic performance and uncertainty, and compare the results to the assessments by a group of dermatologists. By passing duplicates of an image through such a stochastic neural network, we obtained distributions per class, rather than a single probability value. We interpreted the overlap between these distributions as the output uncertainty, where a high overlap indicated a high uncertainty, and vice versa. We had 29 dermatologists diagnose a series of skin lesions and rate their confidence. We compared these results to those of the network. The network achieved a sensitivity and specificity of 50% and 88%, comparable to the average dermatologist (respectively 68% and 73%). Higher confidence/less uncertainty was associated with better diagnostic performance both in the neural network and in dermatologists. We found no correlation between the uncertainty of the neural network and the confidence of dermatologists (R = -0.06, p = 0.77). Dermatologists should not blindly trust the output of a neural network, especially when its uncertainty is high. The addition of an uncertainty score may stimulate the human-computer interaction.

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things.

INTRODUCTION: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. METHOD: This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. RESULTS: The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. CONCLUSION: In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

Impact of artificial intelligence-based color constancy on dermoscopical assessment of skin lesions: A comparative study.

BACKGROUND: The quality of dermoscopic images is affected by lighting conditions, operator experience, and device calibration. Color constancy algorithms reduce this variability by making images appear as if they were acquired under the same conditions, allowing artificial intelligence (AI)-based methods to achieve better results. The impact of color constancy algorithms has not yet been evaluated from a clinical dermatologist's workflow point of view. Here we propose an in-depth investigation of the impact of an AI-based color constancy algorithm, called DermoCC-GAN, on the skin lesion diagnostic routine. METHODS: Three dermatologists, with different experience levels, carried out two assignments. The clinical experts evaluated key parameters such as perceived image quality, lesion diagnosis, and diagnosis confidence. RESULTS: When the DermoCC-GAN color constancy algorithm was applied, the dermoscopic images were perceived to be of better quality overall. An increase in classification performance was observed, reaching a maximum accuracy of 74.67% for a six-class classification task. Finally, the use of normalized images results in an increase in the level of self-confidence in the qualitative diagnostic routine. CONCLUSIONS: From the conducted analysis, it is evident that the impact of AI-based color constancy algorithms, such as DermoCC-GAN, is positive and brings qualitative benefits to the clinical practitioner.

HMT-Net: Transformer and MLP Hybrid Encoder for Skin Disease Segmentation.

At present, convolutional neural networks (CNNs) have been widely applied to the task of skin disease image segmentation due to the fact of their powerful information discrimination abilities and have achieved good results. However, it is difficult for CNNs to capture the connection between long-range contexts when extracting deep semantic features of lesion images, and the resulting semantic gap leads to the problem of segmentation blur in skin lesion image segmentation. In order to solve the above problems, we designed a hybrid encoder network based on transformer and fully connected neural network (MLP) architecture, and we call this approach HMT-Net. In the HMT-Net network, we use the attention mechanism of the CTrans module to learn the global relevance of the feature map to improve the network's ability to understand the overall foreground information of the lesion. On the other hand, we use the TokMLP module to effectively enhance the network's ability to learn the boundary features of lesion images. In the TokMLP module, the tokenized MLP axial displacement operation strengthens the connection between pixels to facilitate the extraction of local feature information by our network. In order to verify the superiority of our network in segmentation tasks, we conducted extensive experiments on the proposed HMT-Net network and several newly proposed Transformer and MLP networks on three public datasets (ISIC2018, ISBI2017, and ISBI2016) and obtained the following results. Our method achieves 82.39%, 75.53%, and 83.98% on the Dice index and 89.35%, 84.93%, and 91.33% on the IOU. Compared with the latest skin disease segmentation network, FAC-Net, our method improves the Dice index by 1.99%, 1.68%, and 1.6%, respectively. In addition, the IOU indicators have increased by 0.45%, 2.36%, and 1.13%, respectively. The experimental results show that our designed HMT-Net achieves state-of-the-art performance superior to other segmentation methods.

Deep Learning-Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement.

Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have some drawbacks, and the prediction is not highly accurate. This is where a dependable automatic recognition system for skin cancers comes into play. With the extensive usage of deep learning in various aspects of medical health, a novel computer-aided dermatologist tool has been suggested for the accurate identification and classification of skin lesions by deploying a novel deep convolutional neural network (DCNN) model that incorporates global average pooling along with preprocessing to discern the skin lesions. The proposed model is trained and tested on the HAM10000 dataset, which contains seven different classes of skin lesions as target classes. The black hat filtering technique has been applied to remove artifacts in the preprocessing stage along with the resampling techniques to balance the data. The performance of the proposed model is evaluated by comparing it with some of the transfer learning models such as ResNet50, VGG-16, MobileNetV2, and DenseNet121. The proposed model provides an accuracy of 97.20%, which is the highest among the previous state-of-art models for multi-class skin lesion classification. The efficacy of the proposed model is also validated by visualizing the results obtained using a graphical user interface (GUI).

Skin Lesion Segmentation in Dermoscopic Images with Noisy Data.

We propose a deep learning approach to segment the skin lesion in dermoscopic images. The proposed network architecture uses a pretrained EfficientNet model in the encoder and squeeze-and-excitation residual structures in the decoder. We applied this approach on the publicly available International Skin Imaging Collaboration (ISIC) 2017 Challenge skin lesion segmentation dataset. This benchmark dataset has been widely used in previous studies. We observed many inaccurate or noisy ground truth labels. To reduce noisy data, we manually sorted all ground truth labels into three categories - good, mildly noisy, and noisy labels. Furthermore, we investigated the effect of such noisy labels in training and test sets. Our test results show that the proposed method achieved Jaccard scores of 0.807 on the official ISIC 2017 test set and 0.832 on the curated ISIC 2017 test set, exhibiting better performance than previously reported methods. Furthermore, the experimental results showed that the noisy labels in the training set did not lower the segmentation performance. However, the noisy labels in the test set adversely affected the evaluation scores. We recommend that the noisy labels should be avoided in the test set in future studies for accurate evaluation of the segmentation algorithms.

Clinical techniques in veterinary dermatology: Dermoscopy.

Dermoscopy is a noninvasive, painless, easy-to-perform technique used in human and veterinary medicine for rapid and magnified in vivo observation of dermatological lesions and disease. Dermoscopy can lead to a swifter diagnosis and may eliminate the need to perform more invasive diagnostic testing such as skin biopsies. To perform dermoscopy, the clinician needs a dermoscope and a software program equipped with image capture for pattern identification. Two techniques exist for dermoscopy: standard contact, where the dermoscope is applied directly to the patient's skin with the use of a liquid interface, or noncontact, where there is no direct contact between the skin and the dermoscope. The most important criteria to be considered when using dermoscopy are the morphology/arrangement of vascular structures, scaling patterns, colours, follicular abnormalities and specific disease features. Application of dermoscopic findings should always be correlated with the patient's history, clinical signs and the morphology of the skin lesions. Dermoscopy does require an initial financial and time investment by the clinician, yet this technique can quickly and easily help to identify patterns of disease that correlate with clinical diagnosis of dermatological disease.

La dermoscopie est une technique non invasive, indolore et facile à réaliser utilisée en médecine humaine et vétérinaire pour l'observation in vivo rapide et agrandie des lésions et maladies dermatologiques. La dermoscopie peut conduire à un diagnostic plus rapide et peut éliminer la nécessité d'effectuer des tests de diagnostic plus invasifs tels que des biopsies cutanées. Pour effectuer une dermoscopie, le clinicien a besoin d'un dermoscope et d'un logiciel équipé d'une capture d'image pour l'identification des motifs. Deux techniques existent pour la dermoscopie : contact standard, où le dermoscope est appliqué directement sur la peau du patient à l'aide d'une interface liquide, ou sans contact, où il n'y a pas de contact direct entre la peau et le dermoscope. Les critères les plus importants à prendre en compte lors de l'utilisation de la dermoscopie sont la morphologie/l'arrangement des structures vasculaires, les schémas de desquamation, les couleurs, les anomalies folliculaires et les caractéristiques spécifiques de la maladie. L'application des résultats dermoscopiques doit toujours être corrélée avec les antécédents du patient, les signes cliniques et la morphologie des lésions cutanées. La dermoscopie nécessite un investissement initial en argent et en temps de la part du clinicien, mais cette technique peut rapidement et facilement aider à identifier les schémas de la maladie en corrélation avec le diagnostic clinique de la maladie dermatologique.

La dermatoscopia es una técnica no invasiva, indolora y fácil de realizar utilizada en medicina humana y veterinaria para la observación in vivo rápida y ampliada de lesiones y enfermedades dermatológicas. La dermatoscopia puede conducir a un diagnóstico más rápido y puede eliminar la necesidad de realizar pruebas de diagnóstico más invasivas, como biopsias de piel. Para realizar la dermatoscopia, el clínico necesita un dermatoscopio y un programa de software equipado con captura de imágenes para la identificación de patrones. Existen dos técnicas para la dermatoscopia: contacto estándar, donde el dermatoscopio se aplica directamente a la piel del paciente con el uso de una interfase líquida, o sin contacto, donde no hay contacto directo entre la piel y el dermatoscopio. Los criterios más importantes que deben tenerse en cuenta al utilizar la dermatoscopia son la morfología/disposición de las estructuras vasculares, los patrones de descamación, los colores, las anomalías foliculares y las características específicas de la enfermedad. La aplicación de los hallazgos dermatoscópicos siempre debe correlacionarse con la historia del paciente, los signos clínicos y la morfología de las lesiones cutáneas. La dermatoscopia requiere una inversión financiera y de tiempo inicial por parte del médico, pero esta técnica puede ayudar rápida y fácilmente a identificar patrones de enfermedad que se correlacionan con el diagnóstico clínico de la enfermedad dermatológica.

A dermatoscopia é uma técnica não invasiva, indolor e de fácil execução utilizada na medicina humana e veterinária para observação in vivo rápida e ampliada de lesões e doenças dermatológicas. A dermatoscopia pode levar a um diagnóstico mais rápido e pode eliminar a necessidade de realizar testes diagnósticos mais invasivos, como biópsias de pele. Para realizar a dermatoscopia, o clínico precisa de um dermatoscópio e um programa de software equipado com captura de imagem para identificação do padrão. Existem duas técnicas de dermatoscopia: contato padrão, onde o dermatoscópio é aplicado diretamente na pele do paciente com o uso de uma interface líquida, ou sem contato, onde não há contato direto entre a pele e o dermatoscópio. Os critérios mais importantes a serem considerados ao utilizar a dermatoscopia são a morfologia/arranjo das estruturas vasculares, padrões de descamação, cores, anormalidades foliculares e características específicas da doença. A aplicação dos achados dermatoscópicos deve sempre ser correlacionada com a história do paciente, os sinais clínicos e a morfologia das lesões cutâneas. A dermatoscopia requer um investimento inicial financeiro e de tempo por parte do clínico, mas esta técnica pode ajudar rápida e facilmente a identificar padrões de doenças que se correlacionam com o diagnóstico clínico de doenças dermatológicas.

Applications of multispectral and hyperspectral imaging in dermatology.

Vision is essential in the diagnostic capabilities in the speciality of dermatology. However, humans are limited in colour vision by the trichromatic visual system that we possess. Multispectral and hyperspectral imaging can overcome this limitation and non-invasively provide novel information about a skin lesion at the cellular level. A literature review from January 2019 to March 2021 for hyperspectral and multispectral imaging in the field of dermatology was conducted. Multispectral/Hyperspectral imaging continues to generate significant research and interest in dermatology. Much of this research is on distinguishing melanoma from benign nevi as this could allow for a diagnosis without biopsy. In addition, adding multispectral/hyperspectral imaging to smartphones is being researched in order to create a portable and low-cost device that can be used in remote areas. One of the limitations in developing devices utilizing hyperspectral imaging has been a sacrifice in specificity in order to maximize sensitivity. Potential solutions to combat this that are being researched include combining multispectral/hyperspectral imaging with other imaging modalities such as photoacoustic imaging in order to overcome the limitations of using each individually. Multispectral/Hyperspectral imaging could be an instrumental aid for clinicians in examining, diagnosing and developing the management plan for patient's skin lesions.

Updated Role of High-frequency Ultrasound in Assessing Dermatological Manifestations in Autoimmune Skin Diseases.

Autoimmune skin diseases are a group of disorders that arise due to the dysregulated immune system attacking self-antigens, causing multiple tissue and organ lesions. With disease progression, the physical and psychological health of patients may be seriously damaged. High-frequency ultrasound is non-invasive, reproducible, and suitable for visualizing the fine structure of external organs. The usage of high-frequency ultrasound has increased in recent years in the auxiliary diagnosis and monitoring of various skin diseases; it serves as a promising tool for dermatological disease assessment. This review summarizes the characteristics of high-frequency ultrasound imaging in common autoimmune skin diseases, including systemic lupus erythematosus, scleroderma, psoriasis, dermatomyositis, and pemphigus/pemphigoid. The objective of this review is to provide new ideas and strategies for dermatologists to diagnose and track the prognosis of autoimmune skin diseases.

Digital skin imaging applications, part I: Assessment of image acquisition technique features.

BACKGROUND: The rapid adoption of digital skin imaging applications has increased the utilization of smartphone-acquired images in dermatology. While this has enormous potential for scaling the assessment of concerning skin lesions, the insufficient quality of many consumer/patient-taken images can undermine clinical accuracy and potentially harm patients due to lack of diagnostic interpretability. We aim to characterize the current state of digital skin imaging applications and comprehensively assess how image acquisition features address image quality. MATERIALS AND METHODS: Publicly discoverable mobile, web, and desktop-based skin imaging applications, identified through keyword searches in mobile app stores, Google Search queries, previous teledermatology studies, and expert recommendations were independently assessed by three reviewers. Applications were categorized by primary audience (consumer-facing, nonhospital-based practice, or enterprise/health system), function (education, store-and-forward teledermatology, live-interactive teledermatology, electronic medical record adjunct/clinical imaging storage, or clinical triage), in-app connection to a healthcare provider (yes or no), and user type (patient, provider, or both). RESULTS: Just over half (57%) of 191 included skin imaging applications had at least one of 14 image acquisition technique features. Those that were consumer-facing, intended for educational use, and designed for both patient and physician users had significantly greater feature richness (p < 0.05). The most common feature was the inclusion of text-based imaging tips, followed by the requirement to submit multiple images and body area matching. CONCLUSION: Very few skin imaging applications included more than one image acquisition technique feature. Feature richness varied significantly by audience, function, and user categories. Users of digital dermatology tools should consider which applications have standardized features that improve image quality.

A Second Case of Localized Cutaneous Amyloid Elastosis: Expanding the Discussion of Associations With a Unique Histopathologic Entity.

ABSTRACT: Amyloid elastosis is an exceedingly rare form of amyloidosis characterized by amyloid material deposited on dermal elastic fibers. Most reported cases have been associated with systemic amyloid light-chain amyloidosis. A single previously reported case of amyloid elastosis showed evidence that the amyloid material was derived from light-chain proteins and was associated with a monoclonal plasma cell infiltrate but failed to demonstrate systemic involvement. As a result, the case was felt to represent localized cutaneous amyloid elastosis. We present a case of localized cutaneous amyloid elastosis that is not associated with a definitive monotypic plasma cell population or with systemic amyloidosis. We also review the clinical and histopathologic features of reported cases of amyloid elastosis and discuss possible etiologic considerations. Because amyloid elastosis can be either localized to the skin or associated with systemic involvement, additional workup to exclude an underlying plasma cell dyscrasia or hematologic malignancy is warranted.

Multicentric Study on High-Frequency Ultrasound Characterization of Calcium Deposits in Dermal and Subcutaneous Calciphylaxis and Calcinosis.

OBJECTIVES: Calcium depositions are frequent in multiple inflammatory dermatosis, they can be explored by ultrasound (US) but the patterns of these depositions have not yet been described. The aim of this study is to describe different patterns of calcium deposition in inflammatory dermatoses. METHODS: The clinical and US data of 58 patients from 7 different centers with inflammatory dermatosis showing ultrasonography-detected calcium depositions was retrospectively reviewed. RESULTS: Dystrophic calcinosis represented 86.2%, calciphylaxis 8.6%, and metastatic calcinosis 5.2%. Three different sonographic patterns of calcium deposition were found: 1) thin hyperechoic bands, parallel to the surface of the epidermis, generating a strong and wide posterior acoustic shadow; 2) hyperechoic spots or lumps with a narrow acoustic shadow; and 3) a linear hyperechoic band parallel to the walls of a blood vessel with also a narrow acoustic shadow. The predominant pattern in metastatic calcifications was type 1, in dystrophic calcifications type 2, and in calciphylaxis type 3. In dystrophic calcinosis, cutis deposits were longer and wider than in calciphylaxis (P < .05). CONCLUSION: New data on inflammatory dermatoses with calcium deposition may be useful for the diagnosis and monitoring of calcium deposits and could avoid the performance of more invasive tests, such as a skin biopsy.

Refined Residual Deep Convolutional Network for Skin Lesion Classification.

Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by dermoscopic analysis. Automated classification of skin lesions is still a challenging task because of the high visual similarity between melanoma and benign lesions. This paper proposes a new residual deep convolutional neural network (RDCNN) for skin lesions diagnosis. The proposed neural network is trained and tested using six well-known skin cancer datasets, PH2, DermIS and Quest, MED-NODE, ISIC2016, ISIC2017, and ISIC2018. Three different experiments are carried out to measure the performance of the proposed RDCNN. In the first experiment, the proposed RDCNN is trained and tested using the original dataset images without any pre-processing or segmentation. In the second experiment, the proposed RDCNN is tested using segmented images. Finally, the utilized trained model in the second experiment is saved and reused in the third experiment as a pre-trained model. Then, it is trained again using a different dataset. The proposed RDCNN shows significant high performance and outperforms the existing deep convolutional networks.

High-frequency ultrasound as a scientific tool for skin imaging analysis.

Ultrasonic imaging is one of the most important diagnostic tools in clinical medicine due to its cost, availability and good correlation with pathological results. High-frequency ultrasound (HFUS) is a technique used in skin science that has been little explored, especially in comparison with other sites and imaging techniques. HFUS shows real-time images of the skin layers, appendages and skin lesions in vivo and can significantly contribute to advances in skin science. This review summarizes the potential applications of HFUS in dermatology and cosmetology, with a focus on quantitative tools that can be used to assess various skin conditions. Our findings showed that HFUS imaging is a reproducible and powerful tool for the diagnosis, clinical management and therapy monitoring of skin conditions. It is also a helpful tool for assessing the performance of dermatological products. This technique may eventually become essential for evaluating the performance of dermatological and cosmetic products.

A plea for standardization of confocal microscopy and optical coherence tomography parameters to evaluate physiological and para-physiological skin conditions in cosmetic science.

Non-invasive reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) have been extended to the dermo-cosmetic field, for skin pathophysiology understanding and therapeutics monitoring. However, standardized methodology and parameters to interpret structures and changes in these settings are still lacking. Present study aimed to propose a validated standard methodology and a list of defined parameters for objective non-pathological skin assessments in the cosmetically sensitive cheekbone area of the face. OCT and RCM quantitative, semi-quantitative and qualitative features were considered for assessments. Validation process included 50 sets of images divided into two age groups. Inter-rater reliability was explored to assess the influence of the proposed methodology. Quantitative OCT parameters of "epidermal thickness," "density and attenuation coefficients" and "vascular density" were considered and calculated. Severity scales were developed for semi-quantitative OCT features of "disruption of collagen" and "vascular asset," while extent scales were produced for semi-quantitative RCM "irregular honeycomb," "mottled pigmentation" and "polycyclic papillary contours." Qualitative assessment was obtained for RCM type of collagen, and comparison between age groups was performed for all features considered. Severity visual scales assistance proved excellent inter-rater agreement across all semi-quantitative and qualitative domains. The assistance of shareable software systems allows for objective OCT quantitative parameters measurement. The use of standard reference scales, within a defined assessment methodology, offers high inter-rater reliability and thus reproducibility for semi-quantitative and qualitative OCT and RCM parameters. Taken together, our results may represent a starting point for a standardized application of RCM and OCT in dermo-cosmetic research and practice.