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.

Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence

BACKGROUND

Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations affect CNN robustness.

OBJECTIVES

To investigate the consistency of melanoma risk assessment by two commercially available CNNs to help formulate recommendations for current clinical use.

METHODS

A comparative cohort study was conducted from January to July 2022 at the Department of Dermatology, University Hospital Basel. Five dermoscopic images of 116 different lesions on the torso of 66 patients were captured consecutively by the same operator without deliberate rotation. Classification was performed by two CNNs (CNN-1/CNN-2). Lesions were divided into four subgroups based on their initial risk scoring and clinical dignity assessment. Reliability was assessed by variation and intraclass correlation coefficients. Excisions were performed for melanoma suspicion or two consecutively elevated CNN risk scores, and benign lesions were confirmed by expert consensus (n = 3).

RESULTS

117 repeated image series of 116 melanocytic lesions (2 melanomas, 16 dysplastic naevi, 29 naevi, 1 solar lentigo, 1 suspicious and 67 benign) were classified. CNN-1 demonstrated superior measurement repeatability for clinically benign lesions with an initial malignant risk score (mean variation coefficient (mvc): CNN-1: 49.5(±34.3)%; CNN-2: 71.4(±22.5)%; p = 0.03), while CNN-2 outperformed for clinically benign lesions with benign scoring (mvc: CNN-1: 49.7(±22.7)%; CNN-2: 23.8(±29.3)%; p = 0.002). Both systems exhibited lowest score consistency for lesions with an initial malignant risk score and benign assessment. In this context, averaging three initial risk scores achieved highest sensitivity of dignity assessment (CNN-1: 94%; CNN-2: 89%). Intraclass correlation coefficients indicated 'moderate'-to-'good' reliability for both systems (CNN-1: 0.80, 95% CI:0.71-0.87, p < 0.001; CNN-2: 0.67, 95% CI:0.55-0.77, p < 0.001).

CONCLUSIONS

Potential user-induced image changes can significantly influence CNN classification. For clinical application, we recommend using the average of three initial risk scores. Furthermore, we advocate for CNN robustness optimization by cross-validation with repeated image sets.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT04605822).

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.

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.

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.

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.

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.

DMpDP: a Diagnostic Multiple-patient DermoFeature Profile store-and-forward teledermoscopy system

Telehealth demand is rapidly growing along with the necessity of providing wide-scale services covering multiple patients at the same time. In this work, the development of a store-and-forward (SAF) teledermoscopy system was considered. The dermoFeatures profile (DP) was proposed to decrease the size of the original dermoscopy image using its most significant features in the form of a newly generated diagonal alignment to generate a small-sized image DP, which is based on the extraction of a weighted intensity-difference frequency (WIDF) features along with morphological features (MOFs). These DPs were assembled to establish a Diagnostic Multiple-patient DermoFeature Profile (DMpDP). Different arrangements are proposed, namely the horizontally aligned, the diagonal-based, and the sequential-based DMpDPs to support the SAF systems. The DMpDPs are then embedded in a recorded patient-information signal (RPS) using a weight factor β to boost the transmitted patient-information signal. The effect of the different transform domains, β values, and number of DPs within the DMpDP were investigated in terms of the diagnostic classification accuracy at the receiver based on the extracted DPs, along with the recorded signal quality evaluation metrics of the recovered RPS. The sequential-based DMpDP achieved the highest classification accuracy, under - 5 dB additive white Gaussian noise, with a realized signal-to-noise ratio of 98.79% during the transmission of 248 DPs using β = 100, and spectral subtraction filtering.

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%.

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.

Utility of Dermoscopy Training in Improving Diagnostic Accuracy of Skin Lesions Among Physician Assistant Students

INTRODUCTION

Patients often first present to their primary care provider for skin lesion concerns, and dermoscopy is a tool that enhances diagnostic acumen of both malignant and benign skin lesions. Physician assistants (PAs) frequently serve as primary care and dermatology providers, but to our knowledge, no current research on dermoscopy expertise with PAs exists. We hypothesize that PA students could be taught dermoscopy based on the triage amalgamated dermoscopic algorithm (TADA) to increase their diagnostic skill, as previously shown with medical students.

METHODS

Dermoscopy was taught to first-year PA students at all 5 PA programs in the state of Minnesota. The training was 50 minutes in length and focused on the fundamentals of the TADA method. Physician assistant students participated in a pretraining and post-training test, consisting of 30 dermoscopic images.

RESULTS

A total of 139/151 (92%) PA students completed both the pretraining and post-training tests. Overall, mean scores for all students increased significantly ( P < .0001) after dermoscopy training was given (18.5 ± 7.1 vs. 23.8 ± 6.7).

CONCLUSION

Our study demonstrates that after TADA training, PA students improved their ability to assess dermoscopy images of both skin cancer and benign lesions accurately, suggesting that PAs can be trained as novice dermoscopists and provide better dermatologic care to patients. We strongly encourage integration of dermoscopy into didactic education across PA programs. Implementing a dermoscopy curriculum in established PA programs will enable future PAs to provide better clinical care when evaluating skin 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.

Demonstration of Convolutional Neural Networks to Determine Patch Test Reactivity

Background: Convolutional neural networks (CNNs) have the potential to assist allergists and dermatologists in the analysis of patch tests. Such models can help reduce interprovider variability and improve consistency of patch test interpretations. Objective: Our aim is to evaluate the performance of a CNN model as a proof of concept in discriminating between patch tests with reactions and patch tests without reactions. Methods: We performed a retrospective analysis of patch test images from March 2020 to March 2021. The CNN model was trained as a binary classifier to discriminate between reaction and nonreaction patches. Performance of the model was determined using summary statistics and receiver operator characteristics (ROC) curves. Results: In total, 13,622 images from 125 patients were recorded for analysis. The majority of patients in the cohort were female (81.6%) with Fitzpatrick skin types I-II (88.0%). The area under curve was 0.940, indicating a high discriminative performance of the model for this data set. This resulted in a total accuracy of 90.1%, sensitivity of 86.0%, and specificity of 90.2%. Conclusions: CNNs have the capacity to determine the presence of delayed-type reactions in patch tests. Future prospective studies are required to assess the generalizability of such models.

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.

Lentigo maligna and lentigo maligna melanoma in vivo differentiation with dermoscopy and reflectance confocal microscopy: A retrospective, multicentre study

INTRODUCTION

Dermoscopic predictors of lentigo maligna (LM) and lentigo maligna melanoma (LMM) have been recently reported, but these have not been reported in reflectance confocal microscopy (RCM).

OBJECTIVES

(i) To validate dermoscopic predictors for LM/LMM, (ii) to identify RCM patterns in LM and LMM, and (iii) correlations between dermoscopic and RCM features in LM and LMM.

MATERIALS AND METHODS

A retrospective, multicentre study of consecutive lesions with histologically proven LM or LMM subtypes of the head and face, with complete sets of dermoscopic and RCM images.

RESULTS

A total of 180 lesions were included (n = 40 LMM). Previously reported differential dermoscopic features for LM subtypes were confirmed. Other features significantly associated with LMM diagnosis included irregular hyperpigmented areas, shiny white streaks, atypical vessels and light brown colour at dermoscopy and medusa head-like structures, dermal nests and nucleated cells within the papillae at RCM (p < 0.05). Correlations among LM lesions between dermoscopic and RCM features included brown to-grey dots and atypical cells (epidermis), grey colour and inflammation and obliterated follicles and medusa head-like structures. Among LMM lesions, significant correlations included obliterated follicles with folliculotropism, both irregular hyperpigmented areas and irregular blotches with widespread atypical cell distribution (epidermis), dermal nests and nucleated cells within the papillae (dermis). Irregular blotches were also associated with medusa head-like structures (dermal epidermal junction [DEJ]).

CONCLUSIONS

Dermoscopic and RCM features can assist in the in vivo identification of LM and LMM and many are correlated. RCM three-dimensional analysis of skin layers allows the identification of invasive components in the DEJ and dermis.

An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection

In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency-based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

Linear alopecia in pediatrics: RCM and dermoscopy reveal diagnostic cues

BACKGROUND

Alopecia areata (AA), trichotillomania (TM), nevus sebaceous (NS), and linear scleroderma en coup de sabre (LSCS) can all present with a patch of linear alopecia, making diagnosis challenging. The purpose of this study was to combine reflectance confocal microscopy (RCM) and dermoscopy in the diagnosis of these lesions in children.

METHODS

A total of 36 patients with linear alopecia were enrolled, of whom 14 had AA, seven had TM, nine had NS, and six had LSCS. We evaluated the characteristics and distinguishing features of the four conditions using RCM and dermoscopy.

RESULTS

The key to differential diagnosis was the dermal Hair follicle density in the dermis was decreased in AA, and the size and density of the follicular openings were normal in TM. In NS, the major features were petal-like and frogspawn-like structures. In LSCS, dermal papillary rings, sebaceous glands, and follicles were partially or completely missing, and abundant fibrous material was distributed in the dermis. Dermoscopy revealed alopecia, and all four conditions resulted in decreased hair density. AA patients exhibited yellow dots, black dots, and exclamation mark hairs. TM patients presented with irregularly broken hairs and blood spots. Both NS and LSCS patients exhibited an absence of follicular openings; NS patients demonstrated whitish and yellowish round structures, while an atrophic area with white patches, linear vessels, and no yellow or black dots was observed in LSCS patients CONCLUSION: RCM combined with dermoscopy can provide additional information on disease states and differentiate between AA, TM, NS, and LSCS.

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.

Challenges in Early Diagnosis of Melanoma Using Tele-Dermatology

Numerous classification systems have been developed over the years, systems which not only provide assistance to dermatologists, but also enable individuals, especially those living in areas with low medical access, to get a diagnosis. In this paper, a Machine Learning model, which performs a binary classification, and, which for the remainder of this paper will be abbreviated as ML model, is trained and tested, so as to evaluate its effectiveness in giving the right diagnosis, as well as to point out the limitations of the given method, which include, but are not limited to, the quality of smartphone images, and the lack of FAIR image datasets for model training. The results indicate that there are many measures to be taken and improvements to be made, if such a system were to become a reliable tool in real-life circumstances.

Unveiling the power of convolutional neural networks in melanoma diagnosis

Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identification of melanomas. Multiple studies have evaluated the performance of convolutional neural networks, and most algorithms match or even surpass the accuracy of dermatologists. However, only 23.8% of dermatologists have good or excellent knowledge of the topic. We believe that the lack of knowledge physicians experience regarding artificial intelligence is an obstacle to its clinical implementation. We describe how a convolutional neural network differentiates a benign from a malignant lesion. We systematically searched the Web of Science, Medline (PubMed), and The Cochrane Library on the 9th February, 2022. We focused on articles describing the role and use of artificial intelligence in melanoma recognition between 2017 and 2022, using the following MeSH terms: "melanoma," "diagnosis," and "artificial intelligence". Traditional machine learning algorithms comprise different parts which must preprocess, segment, extract features and classify the lesion into benign or malignant. Deep learning algorithms can perform these steps simultaneously, which significantly enhances efficiency. Convolutional neural networks include a convolutional layer, a pooling layer, and a fully connected layer. Convolutional and pooling layers extract features from the lesion and reduce computational power, whereas fully connected layers classify the image into two or more categories. Additionally, we suggest that further studies should be performed to accelerate the clinical implementation of artificial intelligence, to create comprehensive datasets and to generate explainable algorithms.

Skin lesion classification using HG-PSO and YOLOv7 based convolutional network in real time

Skin cancer is a chronic illness seen visually and further diagnosed with a dermoscopic examination. It is crucial to precisely localize and classify lesions from dermoscopic images to diagnose and treat skin cancers as soon as possible. This work presents melanoma identification, and the classification method significantly improves accuracy and precision. This work proposes a method Hybrid of Genetic and Particle swarm optimization (HG-PSO), and You only look once version 7 (YOLOv7) based convolutional network for skin cancer classification. The infected region is first located using optimized YOLOv7 object detection. Then color thresholding is applied to segment it, which is passed to the proposed convolutional network for classification. This work is tested on the Human Against Machine with 10,000 training images (HAM10000), International Skin Imaging Collaboration (ISIC)-2019, and Hospital Pedro Hispano (PH2) datasets, and the findings are compared to the state-of-the-art methods for classifying skin cancer. The proposed method achieves 98.86% accuracy, 99.00% average precision, 98.85% average recall, and 98.85% average F1-score on the HAM10000 dataset. It achieves 97.10% accuracy on ISIC-2019 datasets. The average precision obtained is 97.37%, the average recall is 97.13%, and the average F1-score is 97.13% on the ISIC-2019 dataset. It achieves a 97.7% accuracy on the PH2 dataset. The average precision obtained is 99.00%, the average recall is 96.00%, and the average F1-score is 97.00% on the PH2 dataset. The test time taken by this method on datasets HAM10000, ISIC-2019, and PH2 dataset is 2, 3, and 2 s, respectively, which may help give faster responses in telemedicine.

A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization

Nowadays, skin cancer is considered a serious disorder in which early identification and treatment of the disease are essential to ensure the stability of the patients. Several existing skin cancer detection methods are introduced by employing deep learning (DL) to perform skin disease classification. Convolutional neural networks (CNNs) can classify melanoma skin cancer images. But, it suffers from an overfitting problem. Therefore, to overcome this problem and to classify both benign and malignant tumors efficiently, the multi-stage faster RCNN-based iSPLInception (MFRCNN-iSPLI) method is proposed. Then, the test dataset is used for evaluating the proposed model performance. The faster RCNN is employed directly to perform image classification. This may heavily raise computation time and network complications. So, the iSPLInception model is applied in the multi-stage classification. In this, the iSPLInception model is formulated using the Inception-ResNet design. For candidate box deletion, the prairie dog optimization algorithm is utilized. We have utilized two skin disease datasets, namely, ISIC 2019 Skin lesion image classification and the HAM10000 dataset for conducting experimental results. The methods' accuracy, precision, recall, and F1 score values are calculated, and the results are compared with the existing methods such as CNN, hybrid DL, Inception v3, and VGG19. With 95.82% accuracy, 96.85% precision, 96.52% recall, and 0.95% F1 score values, the output analysis of each measure verified the prediction and classification effectiveness of the method.

Mini-Review: The Diagnostic Methods of Tinea Capitis

Tinea capitis is a widespread superficial fungal infection that affects children predominately. Microscopic examination and fungal culture are the conventional gold standards for diagnosis, but they are insensitive and time-consuming. In recent years, new diagnostic methods have been developed to facilitate the diagnosis and identification of causative pathogens. Trichoscopy examination showed high sensitivity and specificity for diagnosing tinea capitis with the characteristic signs of comma hairs, corkscrew hairs, bar code-like hairs and zigzag hairs. Reflectance confocal microscopy has also been used in the rapid diagnosis of tinea capitis in several studies. Molecular assays such as polymerase chain reaction and matrix-assisted desorption/ionization time to flight mass spectrometry are extensively utilized for rapid and accurate identification of the pathogens. Early diagnosis and treatment can aid in disease control and scarring reduction.

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.