In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.

UNASSIGNED: Lentigo maligna (LM) and lentigo maligna melanoma (LMM) are rare in Asian countries. The histopathological diagnosis of LM is often challenging, and misdiagnosis is common. Although histopathologic features of LM/LMM are known, statistical analysis of them were scarcely reported. In this study, we aimed to investigate the histopathological characteristics of LM/LMM in Korean patients and identify key histopathological clues distinguishing LM from benign lentigo.
UNASSIGNED: We performed a retrospective study of the clinical and histopathological features of patients diagnosed with LM/LMM at our center between 2011 and 2022. We assessed the histopathological features in each case based on 16 pathological criteria according to previous literature. Pathologically confirmed cases of benign lentigo were analyzed for comparison.
UNASSIGNED: Twenty-one patients (10 with LM and 11 with LMM) were analyzed. Several statistically significant difference existed between the features of LM and benign lentigo (N = 10), including asymmetry of overall structure (p < 0.001), cytologic atypia (p < 0.001), predominant single-cell proliferation (p < 0.001), melanocytic nests (p = 0.033), melanocytes forming rows (p = 0.003), pagetoid spread of melanocytes (p < 0.001), and hair follicle invasion by atypical melanocytes (p < 0.001). Degree of solar elastosis was more severe in group \"Age ≥ 60\" (p = 0.015), and group \"Diameter ≥ 20 mm\" (p = 0.043). Presence of elongated rete ridges were less common in the older than 60 age group (p = 0.015) and group \"Diameter ≥ 20 mm.\" Invasion was associated with mitosis (p = 0.001, OR 49.285), multinucleated cells (p = 0.035, OR 17.769), and degree of lymphocyte infiltration (p = 0.004).
UNASSIGNED: This study investigated the clinical and histopathologic characteristics of LM and LMM in Koreans. Although histopathological diagnosis is challenging, especially in the early stages of LM, our data showed essential histopathological changes in architectural, cytological, and dermal patterns. Considering the potential aggressiveness of LM/LMM, it is essential to recognize its histopathological features and provide timely management.

A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community.

暂无摘要。

Skin cancer develops due to the unusual growth of skin cells. Early detection is critical for the recognition of multiclass pigmented skin lesions (PSLs). At an early stage, the manual work by ophthalmologists takes time to recognize the PSLs. Therefore, several \"computer-aided diagnosis (CAD)\" systems are developed by using image processing, machine learning (ML), and deep learning (DL) techniques. Deep-CNN models outperformed traditional ML approaches in extracting complex features from PSLs. In this study, a special transfer learning (TL)-based CNN model is suggested for the diagnosis of seven classes of PSLs. A novel approach (Light-Dermo) is developed that is based on a lightweight CNN model and applies the channelwise attention (CA) mechanism with a focus on computational efficiency. The ShuffleNet architecture is chosen as the backbone, and squeeze-and-excitation (SE) blocks are incorporated as the technique to enhance the original ShuffleNet architecture. Initially, an accessible dataset with 14,000 images of PSLs from seven classes is used to validate the Light-Dermo model. To increase the size of the dataset and control its imbalance, we have applied data augmentation techniques to seven classes of PSLs. By applying this technique, we collected 28,000 images from the HAM10000, ISIS-2019, and ISIC-2020 datasets. The outcomes of the experiments show that the suggested approach outperforms compared techniques in many cases. The most accurately trained model has an accuracy of 99.14%, a specificity of 98.20%, a sensitivity of 97.45%, and an F1-score of 98.1%, with fewer parameters compared to state-of-the-art DL models. The experimental results show that Light-Dermo assists the dermatologist in the better diagnosis of PSLs. The Light-Dermo code is available to the public on GitHub so that researchers can use it and improve it.

UNASSIGNED: Esthetic medicine is a rapidly developing field of medicine that is not only beneficial in terms of external appearance, but also significantly improves overall quality of life. Currently, pigmented and vascular skin lesions are more prevalent due to multiple environmental factors and are a characteristic manifestation of skin aging. The development of modern laser therapy has contributed to the successful management of multiple skin conditions. The aim of our study was to show the effect of concomitant reduction of both vascular and hyperpigmented skin lesions located on the facial area after repetitive 532 nm laser therapy and to emphasize that the detection of such observation was possible due to the implementation of System of Skin Analysis and Assessment.
UNASSIGNED: We retrospectively analyzed 100 patients\' records with \"VISIA\" Skin Analysis System after 532nm laser therapy.
UNASSIGNED: Laser therapy significantly decreased VISIA scores for all tested lesions, ie, macules, pigmented and vascular lesions (p<0.0001 for all). The efficacy of laser treatment was not significantly different regarding skin phototype (p>0.05) and did not correlate with age of participants (p>0.05). The more laser sessions were performed, the higher improvement in vascular lesion VISIA scores was observed (r=0.26, p=0.0097).
UNASSIGNED: 532 nm laser therapy is effective regarding vascular and hyperpigmented skin lesions located on the facial area. The System of Skin Analysis and Assessment is a good tool to test the treatment efficacy during regular follow-up procedure.

Today, skin cancer is one of the most common malignant neoplasms in the human body. Diagnosis of pigmented lesions is challenging even for experienced dermatologists due to the wide range of morphological manifestations. Artificial intelligence technologies are capable of equaling and even surpassing the capabilities of a dermatologist in terms of efficiency. The main problem of implementing intellectual analysis systems is low accuracy. One of the possible ways to increase this indicator is using stages of preliminary processing of visual data and the use of heterogeneous data. The article proposes a multimodal neural network system for identifying pigmented skin lesions with a preliminary identification, and removing hair from dermatoscopic images. The novelty of the proposed system lies in the joint use of the stage of preliminary cleaning of hair structures and a multimodal neural network system for the analysis of heterogeneous data. The accuracy of pigmented skin lesions recognition in 10 diagnostically significant categories in the proposed system was 83.6%. The use of the proposed system by dermatologists as an auxiliary diagnostic method will minimize the impact of the human factor, assist in making medical decisions, and expand the possibilities of early detection of skin cancer.

Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany).
We screened open-access dermoscopic image repositories widely used for CNN training for distribution of sex. Moreover, the sex-related diagnostic performance of the market-approved CNN was tested in 1549 dermoscopic images stratified by sex (female n = 773; male n = 776).
Most open-access repositories showed a marked under-representation of images originating from female (40%) versus male (60%) patients. Despite these imbalances and well-known sex-related differences in skin anatomy or skin-directed behaviour, the tested CNN achieved a comparable sensitivity of 87.0% [80.9%-91.3%] versus 87.1% [81.1%-91.4%], specificity of 98.7% [97.4%-99.3%] versus 96.9% [95.2%-98.0%] and ROC-AUC of 0.984 [0.975-0.993] versus 0.979 [0.969-0.988] in dermoscopic images of female versus male origin, respectively. In the sample at hand, sex-related differences in ROC-AUCs were not statistically significant in the per-image analysis nor in an additional per-individual analysis (p ≥ 0.59).
Design and training of artificial intelligence algorithms for medical applications should generally acknowledge sex and gender dimensions. Despite sex-related imbalances in open-access training data, the diagnostic performance of the tested CNN showed no sex-related bias in the classification of skin lesions.

暂无摘要。

Dermatoscopic images are also increasingly used to train artificial neural networks for the future to provide fully automatic diagnostic systems capable of determining the type of pigmented skin lesion. Therefore, fractal analysis was used in this study to measure the irregularity of pigmented skin lesion surfaces. This paper presents selected results from individual stages of preliminary processing of the dermatoscopic image on pigmented skin lesion, in which fractal analysis was used and referred to the effectiveness of classification by fuzzy or statistical methods. Classification of the first unsupervised stage was performed using the method of analysis of scatter graphs and the fuzzy method using the Kohonen network. The results of the Kohonen network learning process with an input vector consisting of eight elements prove that neuronal activation requires a larger learning set with greater differentiation. For the same training conditions, the final results are at a higher level and can be classified as weaker. Statistics of factor analysis were proposed, allowing for the reduction in variables, and the directions of further studies were indicated.