Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy

Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanoma-specific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.

International expert recommendations on image acquisition for in vivo reflectance confocal microscopy of cutaneous tumors

BACKGROUND

No international recommendations exist for a minimum imaging requirement per lesion using reflectance confocal microscopy (RCM). This may be beneficial given the increasing use of remote RCM interpretation internationally.

OBJECTIVE

To develop international expert recommendations for image acquisition using tissue-coupled RCM for diagnosis of cutaneous tumors.

METHODS

Using a modified Delphi approach, a core group developed the scope and drafted initial recommendations before circulation to a larger group, the Cutaneous Imaging Expert Resource Group of the American Academy of Dermatology. Each review round consisted of a period of open comment, followed by revisions.

RESULTS

The recommendations were developed after 5 alternating rounds of review among the core group and the Cutaneous Imaging Expert Resource Group. These were divided into subsections of imaging personnel, recommended lesion criteria, clinical and lesion information to be provided, lesion preparation, image acquisition, mosaic cube settings, and additional captures based on lesion characteristics and suspected diagnosis.

LIMITATIONS

The current recommendations are limited to tissue-coupled RCM for diagnosis of cutaneous tumors. It is one component of the larger picture of quality assurance and will require ongoing review.

CONCLUSIONS

These recommendations serve as a resource to facilitate quality assurance, economical use of time, accurate diagnosis, and international collaboration.

Reflectance Confocal Microscopy Terminology in Spanish: A Delphi Consensus Study

The terminology used to describe reflectance confocal microscopy (RCM) findings in both melanocytic and nonmelanocytic lesions has been standardized in English. We convened a panel of Spanish-speaking RCM experts and used the Delphi method to seek consensus on which Spanish terms best describe RCM findings in this setting. The experts agreed on 52 terms: 28 for melanocytic lesions and 24 for nonmelanocytic lesions. The resulting terminology will facilitate homogenization, leading to a better understanding of structures, more standardized descriptions in clinical registries, and easier interpretation of clinical reports exchanged between dermatologists.

Clinical value of dermoscopy in psoriasis

BACKGROUND

Dermoscopy is a noninvasive technique that has attracted increasing attention in the field of inflammatory skin diseases (such as psoriasis) in recent years.

OBJECTIVE

This study aimed to provide an up-to-date overview of the role of dermoscopy in the diagnosis and extra-diagnosis of psoriasis.

METHODS

This study sought to review the published literature regarding use of dermoscopy in the evaluation of psoriasis.

RESULTS

The diagnostic value of dermoscopy in psoriasis vulgaris, nail psoriasis, and other types of psoriasis was summarized from the aspects of vascular pattern, scale pattern, and other features. Meanwhile, the application value of dermoscopy in the differential diagnosis, efficacy and severity assessment, prediction and monitoring of psoriasis was discussed.

CONCLUSION

Dermoscopy has good clinical value in the diagnosis and differential diagnosis of psoriasis and shows great prospects for severity assessment and efficacy prediction monitoring.

Deeply Supervised Skin Lesions Diagnosis With Stage and Branch Attention

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin diseases. Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment. Recent studies have reported that ensembled convolutional neural networks (CNNs) are practical to classify the images for early diagnosis of skin disorders. However, the practical use of these ensembled CNNs is limited as these networks are heavyweight and inadequate for processing contextual information. Although lightweight networks (e.g., MobileNetV3 and EfficientNet) were developed to achieve parameter reduction for implementing deep neural networks on mobile devices, insufficient depth of feature representation restricts the performance. To address the existing limitations, we develop a new lite and effective neural network, namely HierAttn. The HierAttn applies a novel deep supervision strategy to learn the local and global features by using multi-stage and multi-branch attention mechanisms with only one training loss. The efficacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD2020). The experimental results show that HierAttn achieves the best accuracy and area under the curve (AUC) among the state-of-the-art lightweight networks.

Epidermis lesion detection via optimized distributed capsule neural network

Skin cancer, encompassing various forms such as melanoma, basal cell carcinoma, and others, remains a significant global health concern, often proving fatal if not diagnosed and treated in its early stages. The challenge of accurately diagnosing skin cancer, particularly melanoma, persists even for experienced dermatologists due to the intricate and unpredictable nature of its symptoms. To address the need for more accurate and efficient skin cancer detection, a novel Golden Hawk Optimization-based Distributed Capsule Neural Network (GHO-DCaNN) is proposed. This novel technique leverages advanced computational methods to improve the reliability and precision of skin cancer diagnosis. An optimized clustering-based segmentation approach is introduced, integrating the innovative Sewer Shad Fly Optimization (SSFO), which combines elements of both mayfly and moth flame optimization. This integration enhances the accuracy of lesion boundary delineation and feature extraction. The core of the innovation lies in the optimized distributed capsule neural network, which is trained using the Hybrid GHO. This optimizer, inspired by the behaviors of the golden eagle and fire hawk, ensures the effectiveness of epidermis lesion detection, pushing the boundaries of skin cancer diagnosis methods. The achievements based on the metrics, like specificity, sensitivity, and accuracy show 97.53%, 99.05%, and 98.83% for 90% of training and 97.83%, 99.50%, and 99.06% for k-fold of 10, respectively.

Using Artificial Intelligence as a Melanoma Screening Tool in Self-Referred Patients

INTRODUCTION

Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous.

METHODS

Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised.

RESULTS

Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI's ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images.

CONCLUSION

AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.

Perifollicular linear projections: A dermatoscopic criterion for the diagnosis of lentigo maligna on the face

BACKGROUND

Lentigo maligna (LM) can mimic benign, flat, pigmented lesions and can be challenging to diagnose.

OBJECTIVE

To describe a new dermatoscopic feature termed "perifollicular linear projections (PLP)" as a diagnostic criterion for LM on the face.

METHODS

Retrospective study on reflectance confocal microscopy and dermatoscopy images of flat facial pigmented lesions originating from 2 databases. PLP were defined as short, linear, pigmented projections emanating from hair follicles. Dermatoscopy readers were blinded to the final histopathologic diagnosis.

RESULTS

From 83 consecutive LMs, 21/83 (25.3%) displayed "bulging of hair follicles" on reflectance confocal microscopy and 18 of these 21 (85.7%), displayed PLP on dermatoscopy. From a database of 2873 consecutively imaged and biopsied lesions, 252 flat-pigmented facial lesions were included. PLP was seen in 47/76 melanomas (61.8%), compared with 7/176 lesions (3.9%) with other diagnosis (P < .001). The sensitivity was 61.8% (95% CI, 49.9%-72.7%), specificity 96.0% (95% CI, 92.9%-98.4%). PLP was independently associated with LM diagnosis on multivariate analysis (OR 26.1 [95% CI, 9.6%-71.0]).

LIMITATIONS

Retrospective study.

CONCLUSION

PLP is a newly described dermatoscopic criterion that may add specificity and sensitivity to the early diagnosis of LM located on the face. We postulate that PLP constitutes an intermediary step in the LM progression model.

Dermoscopy and reflectance confocal microscopy finding of different anatomic sites of Langerhans cell histiocytosis

BACKGROUND

Recognizing Langerhans cell histiocytosis (LCH) might be a challenge due to its rarity. Reflectance confocal microscopy (RCM) and dermoscopy were emergent promising non-invasive technique as auxiliary tools in diagnosis of different skin conditions. However, the RCM and dermoscopic features of LCH had been less investigated. To reveal the common RCM and dermoscopic features of LCH.

MATERIALS AND METHODS

Forty cases of LCH were retrospectively analyzed according to age, locations, clinical, RCM, and dermoscopic features from September 2016 to December 2022. To reveal the differences and common in clinical, RCM, and dermoscopic features that occur in different anatomic location.

RESULTS

In the study, sites of predilection include the trunk 31/40 (77.5%), extremity 21/40 (52.5%), face 14/40 (35%), scalp 11/40 (27.5%), vulvar 4/40 (10%), and nail 2/40 (5%). All LCHs had the common RCM features. There were significant differences in clinical and dermoscopic features for age and lesion anatomic site. The common dermoscopic features for scalp, face, trunk, and extremity were the erythematous scaly rash, purplish-red globules or patches, scar-like streaks with ectatic vessels. While the features for nail LCH were purpuric striae, onycholysis and purulent scaly rash, and the erosive erythematous plaque and purulent scaly rash for vulvar LCH. The common RCM features of all LCH showed a focal highly reflective dense image in the surface keratin layer, epidermis architectural disarray, obscuration of dermo-epidermal junction, numerous polygonal, large, medium reflective, short dendrites cells in the epidermis, and dermis. All LCH involving the vulvar and nail did not manifest skin lesions.

CONCLUSION

RCM and dermoscopy showed promising value for diagnosis and differentiation of LCH.

Non-invasive Skin Imaging in Cutaneous Lymphomas

The diagnosis of cutaneous lymphomas is challenging and requires skin tissue for histology and immunophenotyping using immunohistochemistry and molecular studies. In recent years, the role of non-invasive imaging techniques has been described as part of the clinical assessment of cutaneous lymphoma lesions. Imaging modalities such as dermoscopy, reflectance confocal microscopy (RCM), and high frequency ultrasound (HFUS) have been shown to be very valuable in raising the clinical suspicion for lymphomas of the skin, and in distinguishing cutaneous lymphomas from inflammatory dermatoses such as lupus, psoriasis, or eczema. These non-invasive methods can be used to direct the clinician to the optimal biopsy site to maximize the histopathological results and minimize false negatives. These methods also have a potential place in monitoring treatment response. In this review we present a concise summary of the dermoscopic imaging, RCM, and HFUS features seen in cutaneous T-cell lymphomas (CTCL) and B-cell lymphomas (CBCL).

The correlation between dermoscopy and clinical and pathological tests in the evaluation of skin photoaging

BACKGROUND

There are no standards for evaluating skin photoaging. Dermoscopy is a non-invasive detection method that might be useful for evaluating photoaging.

OBJECTIVE

To assess the correlation between the dermoscopic evaluation of photoaging and clinical and pathological evaluations.

METHODS

The age, clinical evaluation (Fitzpatrick classification, Glogau Photoaging Classification, and Chung's standardized image ruler), histopathology (Masson staining and MMP-1 immunohistochemistry), and dermoscopy (Hu's and Isik's) of 40 donor skin samples were analyzed statistically, and Spearman rank correlation analysis was performed.

RESULTS

There was a robust correlation between the total Hu scores and Isik dermoscopy. The correlation of dermoscopy with histopathology was higher than that of clinical evaluation methods. There is a strong correlation between telangiectases and lentigo. Xerosis, superficial wrinkle, diffuse erythema, telangiectases, and reticular pigmentation were significantly correlated with the three clinical evaluation methods. Superficial wrinkles were correlated with Masson, MMP-1, various clinical indicators, and other dermoscopic items.

CONCLUSION

There is a good correlation between dermoscopy and clinical and histopathological examination. Dermoscopy might help evaluate skin photoaging.

Concordance of in vivo reflectance confocal microscopy and horizontal-sectioning histology in skin tumours

BACKGROUND

In vivo reflectance confocal microscopy (RCM) enables the study of architectural and cytological aspects in horizontal sections, which closely correlate with histologic features. However, traditional histopathological vertical sections cannot totally reproduce the image of the in vivo RCM horizontal section.

OBJECTIVE

To evaluate the concordance between in vivo RCM and histopathologic transverse sections for melanocytic lesions, basal cell carcinoma and seborrheic keratoses.

METHODS

Prospectively collected benign melanocytic and non-melanocytic tumours diagnosed by dermoscopy were evaluated for common RCM features and compared to histopathology in horizontal sections with haematoxylin and eosin staining.

RESULTS

A total of 44 skin tumours including 19 melanocytic lesions (nine compound, five junctional and five intradermal nevi), 12 basal cell carcinomas and 13 seborrheic keratoses were collected in the Department of Dermatology of Hospital Clinic of Barcelona. The RCM features that had statistically significant agreement with the histopathological horizontal sections were the preserved and visible honeycomb pattern, well defined DEJ, small bright particles, dermal nests, tumour islands and dark silhouettes, clefting, collagen bundles, thickened collagen bundles and cytologic atypia.

CONCLUSIONS

Histopathology evaluation of horizontal sections of skin tumours can be correlated with main RCM findings. The results of this study have improved the understanding and interpretation of RCM features in relation to skin tumours, thus reinforcing the utility of RCM as a diagnostic tool.

SkinViT: A transformer based method for Melanoma and Nonmelanoma classification

Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early diagnosis and effective treatment. The automated classification of Melanoma and Nonmelanoma is quite challenging task due to presence of high visual similarities across different classes and variabilities within each class. According to the best of our knowledge, this study represents the classification of Melanoma and Nonmelanoma utilising Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) under the Nonmelanoma class for the first time. Therefore, this research focuses on automated detection of different skin cancer types to provide assistance to the dermatologists in timely diagnosis and treatment of Melanoma and Nonmelanoma patients. Recently, artificial intelligence (AI) methods have gained popularity where Convolutional Neural Networks (CNNs) are employed to accurately classify various skin diseases. However, CNN has limitation in its ability to capture global contextual information which may lead to missing important information. In order to address this issue, this research explores the outlook attention mechanism inspired by vision outlooker, which improves important features while suppressing noisy features. The proposed SkinViT architecture integrates an outlooker block, transformer block and MLP head block to efficiently capture both fine level and global features in order to enhance the accuracy of Melanoma and Nonmelanoma classification. The proposed SkinViT method is assessed by different performance metrics such as recall, precision, classification accuracy, and F1 score. We performed extensive experiments on three datasets, Dataset1 which is extracted from ISIC2019, Dataset2 collected from various online dermatological database and Dataset3 combines both datasets. The proposed SkinViT achieved 0.9109 accuracy on Dataset1, 0.8911 accuracy on Dataset3 and 0.8611 accuracy on Dataset2. Moreover, the proposed SkinViT method outperformed other SOTA models and displayed higher accuracy compared to the previous work in the literature. The proposed method demonstrated higher performance efficiency in classification of Melanoma and Nonmelanoma dermoscopic images. This work is expected to inspire further research in implementing a system for detecting skin cancer that can assist dermatologists in timely diagnosing Melanoma and Nonmelanoma patients.

autoSMIM: Automatic Superpixel-Based Masked Image Modeling for Skin Lesion Segmentation

Skin lesion segmentation from dermoscopic images plays a vital role in early diagnoses and prognoses of various skin diseases. However, it is a challenging task due to the large variability of skin lesions and their blurry boundaries. Moreover, most existing skin lesion datasets are designed for disease classification, with relatively fewer segmentation labels having been provided. To address these issues, we propose a novel automatic superpixel-based masked image modeling method, named autoSMIM, in a self-supervised setting for skin lesion segmentation. It explores implicit image features from abundant unlabeled dermoscopic images. autoSMIM begins with restoring an input image with randomly masked superpixels. The policy of generating and masking superpixels is then updated via a novel proxy task through Bayesian Optimization. The optimal policy is subsequently used for training a new masked image modeling model. Finally, we finetune such a model on the downstream skin lesion segmentation task. Extensive experiments are conducted on three skin lesion segmentation datasets, including ISIC 2016, ISIC 2017, and ISIC 2018. Ablation studies demonstrate the effectiveness of superpixel-based masked image modeling and establish the adaptability of autoSMIM. Comparisons with state-of-the-art methods show the superiority of our proposed autoSMIM. The source code is available at https://github.com/Wzhjerry/autoSMIM.

Is reflectance confocal microscopy useful in the differential diagnosis of extra facial lentigo maligna? A retrospective multicentric case-control study

BACKGROUND

Extra facial lentigo maligna (EF-LM) arises outside the head and neck area. EF-LM presents the classic histological features of lentigo maligna. The dermoscopic aspects of EF-LM have been poorly studied.

OBJECTIVE

The primary aims of our study were to analyse and describe the clinical, dermoscopic and confocal microscopy features of a series of histologically confirmed EF-LM.

METHOD

We conducted a retrospective and multicentric study. From our database, we selected 48 cases of thin melanomas on photodamaged skin with histological features of EF-LM of which clinical, dermoscopic and confocal microscopy images were available, and a control group of 45 lesions, that can be subjected to differential diagnosis such as solar lentigo, lichenoid keratosis, seborrheic keratosis and melanocytic nevi, of which dermoscopic and confocal microscope images were available.

RESULTS

Extra facial lentigo maligna had a higher prevalence of lentigo-like pigment patterns, angulated lines and zigzag structures. At confocal microscopy, LM-EF cases showed a higher prevalence of pagetoid spreading, round cells, dendritic cells in the epidermis, atypical cells at the dermo-epidermal junction, dendritic cells at the junction, meshwork pattern and elastosis. Our study shows that reflectance confocal microscopy (RCM) has a sensitivity of 90% and a specificity of 97% for the differential diagnosis of this type of melanoma.

CONCLUSIONS

Extra facial lentigo maligna does not have the classic dermoscopic features of superficial spreading melanoma, the most observed dermoscopic criteria are angulated lines and lentigo-like pigment patterns without lentigo-like border. RCM can be a valuable imaging tool for the evaluation of all those suspicion skin lesions at dermoscopy highlighting cellular atypia suggestive for melanoma.

Spectrophotometric intracutaneous analysis findings and characteristics in plantar verrues

BACKGROUND

Spectrophotometric intracutaneous analysis (SIAscopy) is a non-invasive computerized method that provides insights beyond dermoscopic examination. This study aimed to show the various findings of the plantar verrucae with the SIAscopy evaluation, which displays the chromophores of the skin, melanin, hemoglobin, and collagen.

METHODS

Plantar verrucae lesions scanned using SIAscopy, and their color, total melanin, dermal melanin, collagen, and blood views were recorded on a computer. These SIAgraphs were examined individually and compared to dermoscopic literature findings.

RESULTS

The results of color view were in conformity with dermoscopic findings reported in the literature. Among the findings detected for the first time by a SIAscopic examination, a white halo around the vessel in view of total melanin, effacement in the keratinized areas, and whitening in the vascular areas were noted. There was a mottled collagen condensation, which was believed to be induced by dermal papillomatosis in the lesions in the view of collagen, and consequently, as a result of remittent light reflections due to the collagen structure in this area. There was a clarification in the vascular structures that were noticed in the color view in the blood view. It was noted that vascular structures that have not yet been thrombosed under the thickened epidermis could not be detected in color view.

CONCLUSION

Our study reveals that SIAscopy, a rapid, non-invasive, and easy-to-use examination method similar to dermoscopy, can also diagnose other skin diseases, particularly pathogenetic processes that induce epidermal and papillary dermis changes, apart from pigmented lesions.

Dermoscopy combined with reflectance confocal microscopy as a noninvasive involvement for monitoring Cutaneous Rosai-Dorfman: A case report

Cutaneous Rosai-Dorfman disease(CRDD) is an extremely rare entity and features histiocytic proliferation in the skin. Dermoscopy and reflectance confocal microscopy(RCM) reports on CRDD are rare. We reported a case of CRDD and summarized the dermoscopy(FotoFinder Medicare 800HD, FotoFinder-Systems GmbH, Birbach Germany) and RCM(VivaScope® 1500, V Caliber Imaging and Diagnostics) features of CRDD. The dermoscopic features of CRDD showed red-orange background with pale yellowish roundish areas similar to millet, surrounded by branched blood vessels. Sometimes the white structureless materials of CRDD could be observed by dermoscopy, which may be a hint of spontaneous regression. The RCM features of CRDD revealed dense highly refractile roundish or ovoid structures(inflammatory cells), and multiple larger structures with central low refraction and moderately refractive peripheral semicircle or circle(engulfed inflammatory cells), together with low refractive branched structures(blood vessels). Dermoscopic and RCM features of CRDD can help the dermatologists recognize and follow-up the disease in real time.

Clinician's Ability to Identify Non-Melanoma Skin Cancer on 3D-Total Body Photography Sectors that Were Initially Identified during In-Person Skin Examination with Dermoscopy

INTRODUCTION

Non-melanoma skin cancer (NMSC) is a cause of significant morbidity and mortality in high-risk individuals. Total body photography (TBP) is currently used to monitor melanocytic lesions in patients with high risk for melanoma. The authors examined if three-dimensional (3D)-TBP could be useful for diagnosis of NMSC.

METHODS

Patients (n = 129; 52 female, 77 male) with lesions suspicious for NMSC who had not yet had a biopsy underwent clinical examination followed by examination of each lesion with 3D-TBP Vectra®WB360 (Canfield Scientific, Parsippany, NJ, USA) and dermoscopy.

RESULTS

The 129 patients had a total of 182 lesions. Histological examination was performed for 158 lesions; the diagnoses included basal cell carcinoma (BCC; n = 107), squamous cell carcinoma (SCC; n = 27), in-situ SCC (n = 15). Lesions were located in the head/neck region (n = 138), trunk (n = 21), and limbs (n = 23). Of the 182 lesions examined, 12 were not visible on 3D-TBP; reasons for not being visible included location under hair and on septal of nose. Two lesions appeared only as erythema in 3D-TBP but were clearly identifiable on conventional photographs. Sensitivity of 3D-TBP was lower than that of dermoscopy for BCC (73% vs. 79%, p = 0.327), higher for SCC (81% vs. 74%, p = 0.727), and lower for in-situ SCC (0% vs. 33%, p = 125). Specificity of 3D-TBP was lower than that of dermoscopy for BCC (77% vs. 82%, 0.581), lower for SCC (75% vs. 84%, p = 0.063), and higher for in-situ SCC (97% vs. 94%, p = 0.344). Diagnostic accuracy of 3D-TBP was lower than that of dermoscopy for BCC (75% vs. 80%), lower for SCC (76% vs. 82%), and lower for in-situ SCC (88% vs. 89%). Lesion location was not associated with diagnostic confidence in dermoscopy (p = 0.152) or 3D-TBP (p = 0.353). If only lesions with high confidence were included in the calculation, diagnostic accuracy increased for BCC (n = 27; sensitivity 85%, specificity 85%, diagnostic accuracy 85%), SCC (n = 10; sensitivity 90%, specificity 80%, diagnostic accuracy 83%), and for in-situ SCC (n = 2; sensitivity 0%, specificity 100%, diagnostic accuracy 95%).

CONCLUSION

Diagnostic accuracy appears to be slightly lower for 3D-TBP in comparison to dermoscopy. However, there is no statistically significant difference in the sensitivity and specificity of 3D-TBP and dermoscopy for NMSC. Diagnostic accuracy increases, if only lesions with high confidence are included in the calculation. Further studies are necessary to determine if 3D-TBP can improve management of NMSC.

Dermoscopy of cutaneous sarcoidosis: a cross-sectional study

BACKGROUND

Although traditionally used for the diagnosis of skin tumors, in the past few years dermoscopy as a clinical diagnostic aid for inflammatory and infectious skin manifestations has also received more and more attention. The clinical variability of cutaneous sarcoidosis (CS) often makes its correct diagnosis challenging. Dermoscopy can be used as an auxiliary examination method.

OBJECTIVE

Our aim was to evaluate the role of dermoscopy in the diagnosis and differential diagnosis of CS.

METHODS

This was a retrospective analysis of 39 CS clinical and dermoscopic images collected in the Department of Dermatology, Huashan Hospital Affiliated with Fudan University from August 2013 to February 2021.

RESULTS

Retrospective dermoscopic evaluation revealed small grouped, translucent orange globular structures in all 39 cases. Variable diameter linear vessels were found in 38 cases. A central scar-like area was seen in 26 cases. Bright white streaks were seen in 30 cases. The follicular plugs were seen in 15 cases.

STUDY LIMITATIONS

First, the number of cutaneous sarcoidosis cases the authors collected is small. Second, due to the lack of a control group, the sensitivity and specificity of the proposed criteria were not calculated. Finally, since our study mainly includes suspicious lesions that were biopsied for diagnostic purposes, there may be a selection bias.

CONCLUSION

Lesions showing on dermoscopy grouped translucent orange ovoid structures associated with linear vessels should raise the suspicion of CS. Central scar-like areas and bright white streaks are also helpful in the diagnosis of CS.

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.