Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

From diagnosis to intervention: a review of telemedicine's role in skin cancer care.

Skin cancer treatment is a core aspect of dermatology that relies on accurate diagnosis and timely interventions. Teledermatology has emerged as a valuable asset across various stages of skin cancer care including triage, diagnosis, management, and surgical consultation. With the integration of traditional dermoscopy and store-and-forward technology, teledermatology facilitates the swift sharing of high-resolution images of suspicious skin lesions with consulting dermatologists all-over. Both live video conference and store-and-forward formats have played a pivotal role in bridging the care access gap between geographically isolated patients and dermatology providers. Notably, teledermatology demonstrates diagnostic accuracy rates that are often comparable to those achieved through traditional face-to-face consultations, underscoring its robust clinical utility. Technological advancements like artificial intelligence and reflectance confocal microscopy continue to enhance image quality and hold potential for increasing the diagnostic accuracy of virtual dermatologic care. While teledermatology serves as a valuable clinical tool for all patient populations including pediatric patients, it is not intended to fully replace in-person procedures like Mohs surgery and other necessary interventions. Nevertheless, its role in facilitating the evaluation of skin malignancies is gaining recognition within the dermatologic community and fostering high approval rates from patients due to its practicality and ability to provide timely access to specialized care.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

BACKGROUND: Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. OBJECTIVES: Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. METHODS: We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. RESULTS: A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. CONCLUSIONS: Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

Dermoscopy to differentiate clinically similar inflammatory and neoplastic skin lesions.

INTRODUCTION: Over the few last decades, dermoscopy has become an invaluable and popular imaging technique that complements the diagnostic armamentarium of dermatologists, being employed for both tumors and inflammatory diseases. Whereas distinction between neoplastic and inflammatory lesions is often straightforward based on clinical data, there are some scenarios that may be troublesome, e.g., solitary inflammatory lesions or tumors superimposed to a widespread inflammatory condition that may share macroscopic morphological findings. EVIDENCE ACQUISITION: We reviewed the literature to identify dermoscopic clues to support the differential diagnosis of clinically similar inflammatory and neoplastic skin lesions, also providing the histological background of such dermoscopic points of differentiation. EVIDENCE SYNTHESIS: Dermoscopic differentiating features were identified for 12 relatively common challenging scenarios, including Bowen's disease and basal cell carcinoma vs. psoriasis and dermatitis, erythroplasia of Queyrat vs. inflammatory balanitis, mammary and extramammary Paget's disease vs. inflammatory mimickers, actinic keratoses vs. discoid lupus erythematosus, squamous cell carcinoma vs. hypertrophic lichen planus and lichen simplex chronicus, actinic cheilitis vs. inflammatory cheilitis, keratoacanthomas vs. prurigo nodularis, nodular lymphomas vs. pseudolymphomas and inflammatory mimickers, mycosis fungoides vs. parapsoriasis and inflammatory mimickers, angiosarcoma vs granuloma faciale, and Kaposi sarcoma vs pseudo-Kaposi. CONCLUSIONS: Dermoscopy may be of aid in differentiating clinically similar inflammatory and neoplastic skin lesions.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques.

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.

Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models.

Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients' lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.

Cambios en la dermatoscopia digital en lesiones melanocíticas en 368 pacientes con diagnóstico de síndrome de nevus atípicos y su asociación con el desarrollo de melanoma: estudio de cohorte / Dermoscopic Changes in Melanocytic Lesions in 368 Patients With Atypical Nevus Syndrome and Their Association With Melanoma Incidence: A Cohort Study

Antecedentes y objetivo El síndrome de nevus atípico se ha considerado uno de los factores más importantes para el desarrollo de melanoma. El objetivo de este estudio fue describir los cambios dermatoscópicos de las lesiones melanocíticas en pacientes con diagnóstico de síndrome de nevus atípicos, durante el seguimiento digital en 5 años. Material y métodos Se realizó un estudio retrospectivo de seguimiento a una cohorte de pacientes atendidos en un consultorio particular, especializado en cáncer de piel y mapeo digital corporal, localizado en Medellín (Colombia), entre enero de 2017 y diciembre de 2022. Se analizaron las características dermatoscópicas encontradas y su relación con el diagnóstico de un melanoma. Resultados Se incluyeron 368 pacientes, con una mediana de edad de 43 años RIQ (37-51) de los cuales,187 fueron mujeres. Al finalizar el seguimiento, 222 (60,3%) presentaron red atípica, 163 (44,2%) glóbulos asimétricos, 105 (28,5%) regresión blanco gris, 72 (19,5%) regresión de la lesión, 59 (16%) retículo invertido, 28 (7,6%) pigmento excéntrico asimétrico, 21 (5,7%) proyecciones asimétricas y 8 (2,1%) asimetría en el patrón vascular. A los 60 meses de seguimiento a un 12,2% se les diagnosticó un melanoma. Las áreas blanco-grisáceas, los glóbulos asimétricos, el pigmento excéntrico asimétrico y el retículo invertido fueron las estructuras dermatoscópicas que se relacionaron significativamente con un tiempo menor para la presentación de melanoma (p<0,001, p=0,011, p=0,047 y p=0,001, respectivamente). Conclusiones En conclusión, se encontró que las principales características dermatoscópicas de las lesiones melanocíticas en pacientes con nevus displásicos relacionadas con la progresión a melanoma fueron la aparición de áreas blanco-grisáceas, los glóbulos asimétricos, las manchas asimétricas y el retículo invertido (AU)

Background and objective Atypical nevus syndrome has been described as one of the main risk factors for melanoma. The aim of this study was to analyze dermoscopic changes observed in melanocytic lesions over a follow-up period of 5 years in patients with atypical nevus syndrome. Material and methods We conducted a retrospective follow-up study of a cohort of patients seen at a specialized skin cancer and digital body mapping clinic in Medellin, Colombia, between January 2017 and December 2022. We analyzed the dermoscopic changes observed during this period and explored their association with newly diagnosed melanoma. Results A total of 368 patients (187 women) with a median (interquartile range) age of 43 (37-51) years were included. The dermoscopic features observed at 5 years were an atypical network (222 patients, 60.3%), asymmetric globules (163, 44.2%), white-gray regression areas (105, 28.5%), lesion regression (72, 19.5%), a negative pigment network (59, 16%), asymmetric eccentric pigmentation (28, 7.6%), asymmetric projections (21, 5.7%), and asymmetric vascular patterns (8, 2.1%). Melanoma was diagnosed in 12.2% of patients during follow-up. Features significantly associated with a shorter time to melanoma onset were grayish-white areas (P <.001), asymmetric globules (P=.011), asymmetric eccentric pigmentation (P=.047), and a negative pigment network (P=.001). Conclusions The main dermoscopic features of melanocytic lesions in patients with atypical nevus syndrome associated with progression to melanoma were grayish-white areas, asymmetric globules, asymmetric spots, and a negative pigment network (AU)

[Translated article] Dermoscopic Changes in Melanocytic Lesions in 368 Patients With Atypical Nevus Syndrome and Their Association With Melanoma Incidence: A Cohort Study / Cambios en la dermatoscopia digital en lesiones melanocíticas en 368 pacientes con diagnóstico de síndrome de nevus atípicos y su asociación con el desarrollo de melanoma: estudio de cohorte

Background and objective Atypical nevus syndrome has been described as one of the main risk factors for melanoma. The aim of this study was to analyze dermoscopic changes observed in melanocytic lesions over a follow-up period of 5 years in patients with atypical nevus syndrome. Material and methods We conducted a retrospective follow-up study of a cohort of patients seen at a specialized skin cancer and digital body mapping clinic in Medellin, Colombia, between January 2017 and December 2022. We analyzed the dermoscopic changes observed during this period and explored their association with newly diagnosed melanoma. Results A total of 368 patients (187 women) with a median (interquartile range) age of 43 (37-51) years were included. The dermoscopic features observed at 5 years were an atypical network (222 patients, 60.3%), asymmetric globules (163, 44.2%), white-gray regression areas (105, 28.5%), lesion regression (72, 19.5%), a negative pigment network (59, 16%), asymmetric eccentric pigmentation (28, 7.6%), asymmetric projections (21, 5.7%), and asymmetric vascular patterns (8, 2.1%). Melanoma was diagnosed in 12.2% of patients during follow-up. Features significantly associated with a shorter time to melanoma onset were grayish-white areas (P <.001), asymmetric globules (P=.011), asymmetric eccentric pigmentation (P=.047), and a negative pigment network (P=.001). Conclusions The main dermoscopic features of melanocytic lesions in patients with atypical nevus syndrome associated with progression to melanoma were grayish-white areas, asymmetric globules, asymmetric spots, and a negative pigment network (AU)

Antecedentes y objetivo El síndrome de nevus atípico se ha considerado uno de los factores más importantes para el desarrollo de melanoma. El objetivo de este estudio fue describir los cambios dermatoscópicos de las lesiones melanocíticas en pacientes con diagnóstico de síndrome de nevus atípicos, durante el seguimiento digital en 5 años. Material y métodos Se realizó un estudio retrospectivo de seguimiento a una cohorte de pacientes atendidos en un consultorio particular, especializado en cáncer de piel y mapeo digital corporal, localizado en Medellín (Colombia), entre enero de 2017 y diciembre de 2022. Se analizaron las características dermatoscópicas encontradas y su relación con el diagnóstico de un melanoma. Resultados Se incluyeron 368 pacientes, con una mediana de edad de 43 años RIQ (37-51) de los cuales,187 fueron mujeres. Al finalizar el seguimiento, 222 (60,3%) presentaron red atípica, 163 (44,2%) glóbulos asimétricos, 105 (28,5%) regresión blanco gris, 72 (19,5%) regresión de la lesión, 59 (16%) retículo invertido, 28 (7,6%) pigmento excéntrico asimétrico, 21 (5,7%) proyecciones asimétricas y 8 (2,1%) asimetría en el patrón vascular. A los 60 meses de seguimiento a un 12,2% se les diagnosticó un melanoma. Las áreas blanco-grisáceas, los glóbulos asimétricos, el pigmento excéntrico asimétrico y el retículo invertido fueron las estructuras dermatoscópicas que se relacionaron significativamente con un tiempo menor para la presentación de melanoma (p<0,001, p=0,011, p=0,047 y p=0,001, respectivamente). Conclusiones En conclusión, se encontró que las principales características dermatoscópicas de las lesiones melanocíticas en pacientes con nevus displásicos relacionadas con la progresión a melanoma fueron la aparición de áreas blanco-grisáceas, los glóbulos asimétricos, las manchas asimétricas y el retículo invertido (AU)

Dermatoscopy and reflectance confocal microscopy for basal cell carcinoma diagnosis and diagnosis prediction score: A prospective and multicenter study on 1005 lesions.

BACKGROUND: Basal cell carcinoma (BCC) is usually diagnosed by clinical and dermatoscopy examination, but diagnostic accuracy may be suboptimal. Reflectance confocal microscopy (RCM) imaging increases skin cancer diagnostic accuracy. OBJECTIVE: To evaluate additional benefit in diagnostic accuracy of handheld RCM in a prospective controlled clinical setting. METHODS: A prospective, multicenter study in 3 skin cancer reference centers in Italy enrolling consecutive lesions with clinical-dermatoscopic suspicion of BCC (ClinicalTrials.gov: NCT04789421). RESULTS: A total of 1005 lesions were included, of which 474 histopathologically confirmed versus 531 diagnosed by clinical-dermatoscopic-RCM correlation, confirmed with 2 years of follow-up. Specifically, 740 were confirmed BCCs. Sensitivity and specificity for dermatoscopy alone was 93.2% (95% CI, 91.2-94.9) and 51.7% (95% CI, 45.5-57.9); positive predictive value was 84.4 (95% CI, 81.7-86.8) and negative predictive value 73.3 (95% CI, 66.3-79.5). Adjunctive RCM reported higher rates: 97.8 (95% CI, 96.5-98.8) sensitivity and 86.8 (95% CI, 82.1-90.6) specificity, with positive predictive value of 95.4 (95% CI, 93.6-96.8) and negative predictive value 93.5 (95% CI, 89.7-96.2). LIMITATIONS: Study conducted in a single country. CONCLUSIONS: Adjunctive handheld RCM assessment of lesions clinically suspicious for BCC permits higher diagnostic accuracy with minimal false negative lesions.

Understanding skin color bias in deep learning-based skin lesion segmentation.

BACKGROUND: The field of dermatological image analysis using deep neural networks includes the semantic segmentation of skin lesions, pivotal for lesion analysis, pathology inference, and diagnoses. While biases in neural network-based dermatoscopic image classification against darker skin tones due to dataset imbalance and contrast disparities are acknowledged, a comprehensive exploration of skin color bias in lesion segmentation models is lacking. It is imperative to address and understand the biases in these models. METHODS: Our study comprehensively evaluates skin tone bias within prevalent neural networks for skin lesion segmentation. Since no information about skin color exists in widely used datasets, to quantify the bias we use three distinct skin color estimation methods: Fitzpatrick skin type estimation, Individual Typology Angle estimation as well as manual grouping of images by skin color. We assess bias across common models by training a variety of U-Net-based models on three widely-used datasets with 1758 different dermoscopic and clinical images. We also evaluate commonly suggested methods to mitigate bias. RESULTS: Our findings expose a significant and large correlation between segmentation performance and skin color, revealing consistent challenges in segmenting lesions for darker skin tones across diverse datasets. Using various methods of skin color quantification, we have found significant bias in skin lesion segmentation against darker-skinned individuals when evaluated both in and out-of-sample. We also find that commonly used methods for bias mitigation do not result in any significant reduction in bias. CONCLUSIONS: Our findings suggest a pervasive bias in most published lesion segmentation methods, given our use of commonly employed neural network architectures and publicly available datasets. In light of our findings, we propose recommendations for unbiased dataset collection, labeling, and model development. This presents the first comprehensive evaluation of fairness in skin lesion segmentation.

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.

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.

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.

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.