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This study found that topical fluridil increased the anagen hair percentage in men with androgenetic alopecia without causing irritation or systemic absorption over 9 months.

This study found that the non-steroidal antiandrogen RU58841 increased hair growth rates and promoted hair cycle initiation in balding human scalp grafts on testosterone-conditioned nude mice, indicating a positive effect on hair growth.

This study found that arylhydantoin and arylthiohydantoin derivatives with hydroxybutyl or methyl side-chains showed superior androgen receptor binding affinity and may serve as promising radioiodinated AR ligands.

In this study, using a novel topical androgen receptor suppressor, fluridil, significantly increased the percentage of growing hairs in males with androgenetic alopecia without systemic absorption or adverse side effects.

A new, less toxic and more efficient method to create the anti-baldness compound RU58841 was developed in 2014.

This article discusses the major role of motor vehicles in contributing to urban air pollution and its significant health impacts, especially in developing countries, but reports no new findings.

This paper reviews the current evidence regarding the off-label use of spironolactone for acne management, noting that despite its long-standing application in acne treatment, it has not yet received FDA approval for this indication.

Alopecia can lead to mental health issues, so treatment should address both skin and emotional health.

This article discusses the challenges of diagnosing and managing polycystic ovarian syndrome and reports no new research findings, emphasizing the need for improved diagnostic criteria and lifestyle intervention to prevent complications.

This study found that topical fluridil increased the anagen percentage in men with androgenetic alopecia without causing irritation or systemic side effects.

In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.

This study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

This study introduces a novel method for capturing and digitally rendering the color appearance of physical hair samples using deep neural networks.

In this study, researchers devised a stochastic hair growth model influenced by regular haircuts, using a process that undergoes linear growth subject to random resets, and used it to theoretically determine an ideal haircut routine.

This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.

This study describes a follicular automaton model that simulates human hair cycles and may replicate hair pattern evolution seen in diffuse or androgenetic alopecia.

In this study, a novel GAN-based image translation method focusing on regions of interest showed improved predictive performance for post-hair transplant images compared to existing methods, using an ensemble approach to enhance robustness and detection accuracy.

This study presents a new hair removal algorithm for dermatoscopic images of skin lesions that improves hair detection accuracy by 2–7% and hair repair accuracy by 2–5% on average, using advanced techniques like maximum variance fuzzy clustering, Criminisi priorities, and the ant colony algorithm.

This study found that GPT-4o demonstrated high concordance with in-person and image-based provider assessments in generating SALT scores for alopecia areata, suggesting its potential as an adjunct tool in clinical practice for automating these assessments.

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 observed that GPT-4o, a generative AI model, showed high concordance with human providers in automating image-based SALT scores for assessing alopecia areata, suggesting its potential in assisting clinical evaluations without additional training.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

ChatGPT is preferred for creating dermatology patient handouts, but all models can be useful with oversight.

This study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.

This study found that the multi-thresholding method was the most effective for segmenting hair during laser removal, as it clearly distinguished hair patterns with minimal noise.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

Androgenetic alopecia is the only hair loss condition with specific diagnostic criteria in trichoscopy.

This study proposes a method for automatically removing hairs from skin lesion images by using edge-tangent flow for hair detection and texture synthesis for restoring occluded regions with minimal artifacts.

This study presented a new algorithm and camera-based system that successfully reconstruct high-quality, 3D representations of facial hair and underlying skin surfaces even in dense regions like eyebrows.