Basal cell carcinoma (BCC) accounts for 75% of all skin cancers. Currently, all major public hospitals in Spain have a dermatology care protocol that includes teledermatology. This has created an overload for hospital dermatologists, which could be alleviated with an AI tool for prioritization. Several AI systems have been proposed for this purpose, but the lack of transparent diagnostic explanations limits their clinical acceptance and implementation. This fact motivates the present study, which aims to develop an AI tool focused on detecting BCC from dermoscopic images incorporating dermatologist diagnostic criteria to enhance reliability. Specifically, the BCC diagnostic criterium is that a lesion is not considered BCC if it exhibits pigment network pattern, and that a lesion is considered BCC if it exhibits at least one of these BCC patterns: ulceration, ovoid nest, multi globules, maple-leaf, spoke wheel, arborizing telangiectasia. We analyzed 1,559 dermoscopic images collected from 60 primary care centers in Andalusia. Four dermatologists annotated the images as exhibiting or not each of the seven possible BCC patterns. As there is no established Ground Truth to determine the BCC patterns present in a lesion, we propose an Expectation-Maximization consensus algorithm to consolidate the multi-rater annotations into a unified standard reference (SR). As an additional novelty, the system incorporates the symbolic reasoning of dermatologists, who base their diagnoses on BCC patterns shown in lesions. To this end, a multitask learning (MTL) system based on MobileNet-V2 was designed. This system can rapidly triage BCC and non-BCC lesions while providing clinical information justifying this classification. This system also provides GradCAM-based maps to dermatologists to improve its reliability and confidence. Three evaluations were performed on the AI system. First, a performance analysis was conducted to evaluate the AI tool’s ability to classify lesions as BCC or non-BCC. In this analysis, the model achieved 90% accuracy (precision=0.90, recall=0.89). The second evaluation analyzed whether the detected patterns agreed with dermoscopic criteria. Notably, at least one clinically relevant BCC pattern was correctly identified in 99% of BCC-positive cases, and the pigment-network negative criterion was met in 95% of non-BCC cases. A comparison of the GradCAM maps with the dermatologist’s manual delineation demonstrated strong colocalization with dermatologist-segmented regions (mean foreground density 0.57 vs. background 0.16), confirming alignment of the visual focus of experts. This work introduces the first clinically validated dual-explanation AI system that combines high-accuracy BCC detection with transparent, pattern-based explanations. This approach closes the critical gap between AI performance and clinical trust in teledermatology, positioning the system for immediate deployment in primary care. Future work will focus on determining the extent to which this dual explanation system improves dermatologists’ confidence.

This study aims to analyze dermoscopic images to estimate the Breslow depth of melanoma, which is crucial for diagnosing and treating skin cancer. The study has two objectives: to improve current methods of estimating melanoma depth and to investigate whether dermoscopic features evolve as melanoma thickness increases.To achieve these goals, a Convolutional Neural Network (CNN), with ConvNext as backbone, was designed and trained to predict melanoma thickness. Then, the relationships between the CNN’s outputs and the continuous depth values from biopsies were explored to investigate possible gradations in deep features related to melanoma depth. First, statistical analyses were performed in the deep feature spaces. Secondly, deep features were transformed into Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) spaces.Two main results can be reported. First, the model outperformed existing methods in the literature for classifying melanomas as superficial or deep. It achieved the highest recall (0.79) and F1 score (0.67), a good balance between precision and recall. As for the relationships between deep features and biopsy depth, a moderate correlation was found between melanoma depth and prediction probabilities, which was stronger in deeper melanomas. Both PCA and UMAP analyses revealed that intermediate-depth melanomas have deep features located in intermediate positions in the transformed feature space.These findings highlight the capacity of deep features to distinguish between different depths of melanoma. The study provides new insights into the data distribution and suggests a potential methodology for enhancing the understanding of melanoma classification through deep learning models.

Significance:Infantile hemangiomas are one of the most prevalent benign tumors in childhood. Typically, diagnosis relies on visual assessment of area, texture, and color. A few studies have focused on various color attributes in superficial and mixed Infantile hemangioma types, neglecting the deep category. Limited research has explored temperature in the location of hemangioma lesions. Aim:We seek, for the first time, to quickly identify and classify infantile hemangioma lesions using a portable, programmable handheld device. The system aims to (1) replicate a physician's assessment of infantile hemangioma and (2) deliver an easy way to understand automatic diagnosis. Approach:The custom-built device comprises an infrared sensor and a visible light spectrum sensor to assess color and depth through computations of different color and texture features. Over a 3-year period, 53 patients were monitored, and 83 hemangioma images were captured. Results:The device accurately localized all lesions in real time and classified hemangioma lesions into three primary types using selected color and texture features. Evaluation metrics showed an average sensitivity of 0.8948 and specificity of 0.7313 for an accuracy of 0.7572 and an average sensitivity of 0.7803 and specificity of 0.8720 for an F -score of 0.7826 in the three-class classification. Conclusions:The two-sensor device accurately identifies and categorizes infantile hemangioma lesions, providing a clear automated diagnosis based on computerized features.

BACKGROUND:Reliable labels are essential when training Artificial Intelligence (AI) tools. Whereas some diseases allow biopsy-based labeling, others rely on subjective criteria. For the diagnosis of basal cell carcinoma (BCC), dermatologists detect certain dermoscopic criteria, whose presence (or absence) serves as the basis for determining a diagnosis of BCC. Therefore, an AI tool assisting in BCC diagnosis should provide such criteria to explain its output. MATERIALS AND METHODS:This study analyzes the agreement among four dermatologists in detecting dermoscopic criteria and compares the performance of an AI model trained with labels from a single dermatologist versus a consensus-based standard. A total of1230 dermoscopic images, collected in around 60 primary health centers, sent via teledermatology, and diagnosed by four dermatologists, were used to train an AI tool. They were randomly selected from the teledermatology platform (2019-2021). Subsequently, 204 new images were used to test the AI tool prospectively. A standard reference (SR) was built using Expectation Maximization on the four diagnoses. The performance of the AI tool trained using the reference standard of one dermatologist versus the reference standard statistically inferred from the consensus of four dermatologists was analyzed using McNemar's test and Hamming distance. RESULTS:Agreement among dermatologists was high for BCC versus non-BCC (Kappa = 0.9079; PPV = 0.9670), but lower for specific criteria. Statistical differences were found in the performance of AI models trained with individual and consensus labels. CONCLUSION:Deriving an SR from multiple expert opinions mitigates individual bias and enhances AI interpretability, key for its clinical adoption.

Deep learning has transformed computer vision but relies heavily on large labeled datasets and computational resources. Transfer learning, particularly fine-tuning pretrained models, offers a practical alternative; however, models pretrained on natural image datasets such as ImageNet may fail to capture domain-specific characteristics in medical imaging. This study introduces an unsupervised learning framework that extracts high-value dermatological features instead of relying solely on ImageNet-based pretraining. We employ a Variational Autoencoder (VAE) trained from scratch on a proprietary dermatological dataset, allowing the model to learn a structured and clinically relevant latent space. This self-supervised feature extractor is then compared to an ImageNet-pretrained backbone under identical classification conditions, highlighting the trade-offs between general-purpose and domain-specific pretraining. Our results reveal distinct learning patterns. The self-supervised model achieves a final validation loss of 0.110 (-33.33 ImageNet-pretrained model stagnates at 0.100 (-16.67 Accuracy trends confirm this: the self-supervised model improves from 45 65 model reaches 87 gap increasing to +0.060. These findings suggest that while ImageNet pretraining accelerates convergence, it also amplifies overfitting on non-clinically relevant features. In contrast, self-supervised learning achieves steady improvements, stronger generalization, and superior adaptability, underscoring the importance of domain-specific feature extraction in medical imaging.

The number of artificial intelligence (AI) systems developed for skin lesion classification has grown rapidly, notably driven by the release of publicly available datasets, such as those from the ISIC Archive. While this accessibility has allowed many research groups, often without a medical background, to train deep learning algorithms under ideal conditions, these often fail to maintain performance in real-world clinical environments where variations in image quality reveal limited generalizability. To address this gap, we propose a reinterpretation of the traditional lesion classification task by prioritizing clinical urgency and categorizing lesions into three priority levels rather than assigning a specific diagnosis. We developed a customized ConvNeXt-Tiny model, implemented specific techniques to handle class imbalance, and trained it on a combination of two public datasets, ISIC 2019 and Hospital Italiano de Buenos Aires, and a large private real-world dataset from Virgen Macarena University Hospital (Seville, Spain). In parallel, an exhaustive investigation of existing AI systems for skin lesions was also carried out, and two representative models were finally selected for adaptation to the task. Although both systems performed effectively in their original diagnostic contexts, a considerable drop in accuracy was observed when evaluated on our threelevel framework. In contrast, our proposed system demonstrates significant performance and specifically achieves improvements in accuracy from 13 % to 21 % at the evaluated priority levels. These results highlight the importance of adapting AI tools to realistic triage tasks and confirm the potential of our method as a possible practical assistance tool in dermatology.

Objective: To analyze the frequency and co-occurrence of dermoscopic patterns in BCC lesions and their relationship with histopathologic subtypes, using statistical analysis and Information Theory tools such as entropy, conditional entropy, mutual information, and Hamming weight. Methods: A total of 223 dermoscopic images (256x256 pixels) of histologically confirmed BCC lesions from Hospital Universitario Virgen Macarena (Seville, Spain) were analyzed. Each image was multilabel-annotated for the presence of nine dermoscopic patterns and categorized into one of four BCC subtypes: superficial, nodular, infiltrative, or micronodular. Statistical and information-theoretic methods were applied, including co-occurrence matrices, Bayesian conditional probabilities, and entropy-based metrics. Mutual information quantified the predictive value of individual and paired patterns, and decision trees were built based on diagnostic informativeness. Results: Nodular BCC was highly associated with most dermoscopic patterns, particularly arborizing telangiectasia and blue-gray ovoid nests. Superficial BCC showed stronger associations with maple leaf-like structures and shiny white-red areas. Some patterns, like spoke-wheel areas and white streaks, showed low discriminative power. Mutual information and conditional probabilities identified meaningful pattern-pair dependencies for each subtype. Decision trees revealed that subsets of patterns could enhance subtype classification by accumulating diagnostic information. Conclusions: Information Theory enables a quantitative understanding of dermoscopic patterns and their relationship with BCC subtypes. This framework highlights key diagnostic features, aids in differentiating complex cases, and supports the development of automated, pattern-based diagnostic tools. Further research with larger, more balanced datasets is encouraged.

Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced lung cancer, as it has been shown to improve survival outcomes. However, it is associated with increased toxicity rates, particularly hematologic toxicity, mainly due to radiation-induced damage to the bone marrow. This study developed a fully automated pipeline to predict the risk of nine clinically relevant grade 2-3 cytopenias by integrating clinical data with bone marrow-derived radiomic features. Chest CT scans from 698 patients were processed using an automated skeleton-marrow segmentation pipeline (mean Dice=0.776). From the resulting masks, 12,218 radiomic features were extracted with PyRadiomics and combined with 69 clinical variables. A three-step reduction strategy (statistical filtering, correlation pruning, mutual information) yielded 200-600 nonredundant features per endpoint. Univariate analyses revealed that a focused subset of radiomic descriptors, particularly those capturing intensity distributions, textural heterogeneity, and morphological patterns, consistently demonstrated strong associations with hematologic toxicities. These features showed a promising predictive performance across multiple endpoints, especially for higher-grade toxicities, supporting their relevance as early markers of adverse effects.

Melanoma, a highly aggressive form of skin cancer, is primarily assessed by its depth of invasion (Breslow thickness), a key prognostic factor influencing clinical management. Deep learning has shown promising results in dermatological image analysis, yet its effectiveness is often hindered by overfitting due to limited and imbalanced datasets. Knowledge Distillation (KD) has emerged as a potential solution, enabling the transfer of knowledge from a large, complex teacher model to a smaller, more efficient student model. However, the impact of using domain-specific datasets for distillation remains underexplored in medical imaging.In this study, we investigate the role of dataset specificity in knowledge distillation for melanoma depth classification. We compare two KD approaches: one using a general-purpose dataset (ImageNet) and another using a dermatology-specific dataset. Performance is evaluated through classification metrics, feature visualization techniques (UMAP and t-SNE), Grad-CAM interpretability maps, and robustness tests under image perturbations. Our findings reveal that while ImageNet-distilled models achieve slightly better classification performance, the dermatology-specific distillation provides more structured feature representations, suggesting improved task-specific knowledge retention. Additionally, models trained with domain-specific KD demonstrate enhanced resilience to real-world variations, such as image noise and brightness changes.These results highlight the potential of tailored knowledge distillation strategies in medical imaging, suggesting that leveraging domain-specific datasets can improve model interpretability and robustness while mitigating overfitting. Future work will explore hybrid distillation techniques and domain adaptation strategies to further enhance deep learning applications in dermatological oncology.

This study focuses on analyzing dermoscopy images to determine the depth of melanomas, which is a critical factor in diagnosing and treating skin cancer. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. This research aims to improve the prediction of the depth of melanoma through the use of machine learning models, specifically deep learning, while also providing an analysis of the possible existance of graduation in the images characteristics which correlates with the depth of the melanomas. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. The datasets were combined and balanced to ensure robust model training. The study utilized pre-trained Convolutional Neural Networks (CNNs). Results indicated that the models achieved significant improvements over previous methods. Additionally, the study conducted a correlation analysis between model's predictions and actual melanoma thickness, revealing a moderate correlation that improves with higher thickness values. Explainability methods such as feature visualization through Principal Component Analysis (PCA) demonstrated the capability of deep features to distinguish between different depths of melanoma, providing insight into the data distribution and model behavior. In summary, this research presents a dual contribution: enhancing the state-of-the-art classification results through advanced training techniques and offering a detailed analysis of the data and model behavior to better understand the relationship between dermoscopy images and melanoma thickness.

Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is needed to train an artificial intelligence (AI) tool that explains the BCC diagnosis by providing its dermoscopic features. Objectives: To determine the consensus among dermatologists on dermoscopic criteria of 204 BCC. To analyze the performance of an AI tool when the ground-truth is inferred. Methods: A single center, diagnostic and prospective study was conducted to analyze the agreement in dermoscopic criteria by four dermatologists and then derive a reference standard. 1434 dermoscopic images have been used, that were taken by a primary health physician, sent via teledermatology, and diagnosed by a dermatologist. They were randomly selected from the teledermatology platform (2019-2021). 204 of them were tested with an AI tool; the remainder trained it. The performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists was analyzed using McNemar's test and Hamming distance. Results: Dermatologists achieve perfect agreement in the diagnosis of BCC (Fleiss-Kappa=0.9079), and a high correlation with the biopsy (PPV=0.9670). However, there is low agreement in detecting some dermoscopic criteria. Statistical differences were found in the performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists. Conclusions: Care should be taken when training an AI tool to determine the BCC patterns present in a lesion. Ground-truth should be established from multiple dermatologists.

Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised ones.

Infantile hemangiomas occur in 3 to 10% of infants. To predict the clinical course and counsel on treatment, it is crucial to accurately determine the hemangiomas’ extension, volume, and location. However, this can represent a challenge because hemangiomas may present irregular patterns or be covered by hair, or their depth may be difficult to estimate. Diagnosis is commonly made by clinical inspection and palpation, with physicians basing their diagnoses on visual characteristics such as area, texture, and color. Doppler ultrasonography or magnetic resonance imaging are normally used to estimate depth or to confirm difficult assessments. This paper presents an alternative diagnosis tool—thermography—as a useful, immediate means of carrying out accurate hemangioma examinations. We conducted a study analyzing infantile hemangiomas with a custom thermographic system. In the first phase of the study, 55 hemangiomas of previously diagnosed patients were analyzed with a thermal camera over several sessions. An average temperature variation before and after treatment of −0.19 °C was measured. In the second phase, we selected nine patients and assessed their evolution over nine months by analyzing their thermographic images and implementing dedicated image processing algorithms. In all cases, we found that the thermal image analysis concurred with the independent diagnoses of two dermatologists. We concluded that a higher temperature inside the tumor in the follow-up was indicative of an undesirable evolution.

Background and Objective. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. Methods. The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. Results. Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. Conclusions. To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.

Background and objectives: Several studies have addressed the relationship between bipolar disorder and meteorological variables, but no previous review focusing on the influence of a wide range of meteorological variables on bipolar disorder has been published. The aim of this study is to conduct a systematic review about the influence of weather on the clinical course of bipolar disorder patients. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the main electronic medical databases were searched in February 2020, and studies were screened based on the eligibility criteria. 24 studies were selected for qualitative synthesis. Most of them were observational retrospective studies based in medical records. Results: The most studied meteorological variables were temperature and sunlight, and the most studied clinical outcomes were hospital admissions. Significant correlations were found between temperature and sunlight and clinical outcomes, although the findings were heterogeneous. Higher temperatures may trigger bipolar disorder relapses that require hospital admission, and higher expositions to sunlight may increase the risk of manic episodes. Conclusion: Meteorological variables seem to have an influence in the course of bipolar disorder, especially temperature and sunlight, although further studies are needed to clarify this possible relationship. (C) 2021 Asociacion Universitaria de Zaragoza para el Progreso de la Psiquiatria y la Salud Mental. Published by Elsevier Espana, S.L.U. AU rights reserved.

The segmentation of bone, fat and muscle tissue in CT volumes is of special interest for surgeons and radiologists in order to diagnose some diseases and/or in surgery planning. These tissues are very difficult to delineate due to the presence of multiple and different structures and the similarity in terms of Hounsfield values with surrounding organs. In this paper, an automatic algorithm to implement the segmentation of bone, muscle and fat tissue is shown. The segmentation is carried out by minimizing an energy function via convex relaxation. In previous works carried out by the authors, only two labels had been considered (bone and muscle) and the methods had strong problems to segment skeletal muscle accurately due to the presence of internal organs with Hounsfield values similar to those within muscle tissue. In the present work, prior knowledge about the distribution of skeletal muscle in abdominal, chest and pelvis CT volumes has been considered by including a binary distance transform in the computation of the cost terms. Furthermore, a third label corresponding to fat tissue has been included in the segmentation. A public database has been used to assess the performance of the algorithm. Different metrics such as DICE, Jaccard, specificity, sensitivity and Positive Predictive Value (PPV) indexes have been obtained to evaluate the algorithm performance The technique has been compared with a previous multi-label scheme, a Hybrid level-set model implementation and a thresholding algorithm. The algorithm proposed outperformed the other methods in all the metrics considered.

Vascular malformations classification may pose a diagnostic challenge for physicians. In the early stages, they are diagnosed clinically mainly by visual inspection. For a deeper analysis, Doppler ultrasonography is the preferred technique to determine the haemodynamic behaviour of the anomaly. However, this imaging method is not always available and it requires trained operators to acquire and interpret the images. There is a lack of portable and user-friendly systems that may help physicians in the assessment of vascular malformations. We propose a new diagnostic procedure, more affordable and easier to use, based on a portable thermal camera. This technique provides information about temperature, which has been found to be correlated with the flow rate of the lesion. In our study, > 60 vascular malformations of previously diagnosed patients were analysed with a thermal camera to classify them into low-flow and high-flow malformations. The value was 1 for both sensitivity and specificity of this technique.