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This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

This study developed new genetic prediction models for male pattern baldness with improved accuracy by utilizing a large set of markers and independent datasets, making them the most reliable available for this trait.

This database provides detailed physiological parameters of porcine skin to enhance the MPML MechDermA Model, improving dermal absorption predictions for human studies in the Simcyp Simulator.

This study examined data from the FDA Adverse Event Reporting System and found that finasteride use is linked with both known and previously unlisted adverse events, such as erectile dysfunction and post-5α reductase inhibitor syndrome, with variations observed by age and sex.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This study evaluated a new computer-aided detection system for identifying Breast Arterial Calcification in mammograms, achieving 70% accuracy, but highlighted the need for a larger dataset to explore its relationship with cardiovascular diseases.

This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

This study reported that the PM-DBiGRU model enhances aspect-level sentiment classification in drug reviews, outperforming existing methods on the newly proposed SentiDrugs dataset.

This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.

This study introduces a novel image enhancement method that significantly improves the visual quality of low-light sports images by utilizing improved bilateral filtering and the CLAHE algorithm, achieving a 65.24% improvement in color and edge detail preservation compared to state-of-the-art methods on the LOL dataset.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

In this study, the DIET-AI model, developed from a large dataset of over 200,000 images, demonstrated diagnostic performance for 31 skin diseases comparable to dermatologists of varying experience levels in 15 hospitals across China, supporting its potential effectiveness in clinical settings.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

This study analyzed gene expression data to identify key genes involved in severe forms of alopecia areata, discovering four immune monitoring genes (LGR5, SHISA2, HOXC13, S100A3) with potential for early diagnosis and better understanding of the disease's biological mechanisms.

This study suggests that variations in PCOS phenotypes observed across different ethnic groups may be due to a genetic gradient resulting from historical human migrations and genetic drift.

In this study, researchers are constructing a comprehensive data set from 997 adult participants in Japan to develop a statistical model that explores the relationships among various health parameters, aiming to enable personalized and preventive health care interventions based on empirical data.

This study introduces a novel computational model using fuzzy logic and multi-criteria decision-making techniques to create a triage system for androgenetic alopecia management, stratifying patients into seven severity levels and aiding in resource allocation and treatment planning.

This study introduces the Hair Cell Analysis Toolbox (HCAT), a machine-learning software that automates the analysis of cochlear hair cells, enabling unbiased and comprehensive imaging data interpretation.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

This study suggests that novel diagnostic, preventive, and treatment approaches for autoimmune diseases like alopecia areata may be developed by identifying hub genes, and highlights the usefulness of machine learning and bioinformatics in finding new disease biomarkers.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

This study developed an automated image analysis framework for diagnosing hair disorders using trichoscopic images, reporting a Random Forest classifier as having an 86.67% accuracy in distinguishing between different scalp pathologies based on quantitative image features.

This study aims to use a comprehensive health data set to develop a statistical model that can improve personalized and preventive health care by understanding relationships between various health parameters in individuals.

This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.