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This study developed a quantitative measure for scoring hair surface damage from SEM images and found it accurately classified hair damage caused by explosive blasts, similar to an existing classification system.

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 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 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.

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 utilized magnetic resonance imaging to noninvasively visualize and differentiate skin structures in rat skin, finding a significant correlation between MRI data and histological areas.

This study found that hair proteome profiling and genetically variant peptide identification in hairs remained effective after an explosive blast, indicating potential for forensic human identification despite damage.

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.

This study found that Piezo2 channels are primarily located on sensory axon membranes in mechanosensory end organs, supporting a model where mechanical stimuli activate Aβ RA-LTMR neurons via axon protrusions.

This study found that vimentin is essential for proper wound healing and cell growth by influencing EMT signaling and mTOR activity in mice.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This study found that VE-cadherin and Alk1, traditionally linked to vascular functions, also play crucial roles in maintaining nerve homeostasis in mice during hair growth cycles by modulating certain cell populations.

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.

Pollution exposure speeds up hair damage.

This study demonstrated that intraoperative bioprinting using a bioink with human adipose-derived extracellular matrix and stem cells achieved successful reconstruction of full-thickness craniomaxillofacial skin defects in rats, promoting wound closure, adipogenesis, and hair follicle-like structure formation within two weeks.

This study found that the p63 transcription factor plays a role in epidermal morphogenesis by regulating Satb1 expression, impacting chromatin architecture and gene expression in epidermal progenitor cells.

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

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.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

In this study, JAUN soap was found to improve skin conditions, such as moisture, elasticity, and cleansing effectiveness, without adverse reactions, suggesting its potential as a safe and effective ingredient for functional cosmetics.

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

This study identified key cells and pathways needed for the development of touch receptor patterns in mouse skin, notably that certain keratinocytes are crucial for innervation patterns, while Merkel cells and BMP signaling have distinct roles.

Image analysis can effectively identify changes in scalps affected by chemotherapy-induced hair loss.

This study discovered a new region in the hair cortex where intermediate filaments are both aligned with the hair's axis and orientationally ordered, influenced by the cuticle boundary.

In this study, UVB exposure was found to alter certain chemical properties of the hair shaft, such as keratin components and water content, without significantly affecting its physical morphology.

This study reports that novel core-ABS nanocarriers efficiently loaded with dexamethasone and finasteride show potential for dermal drug delivery, exhibiting non-toxic interactions with human keratinocyte cells and skin penetration.

In this study, human gingival mesenchymal stem cells responded differently to inflammation mediated by oxidized low-density lipoprotein (oxLDL) compared to cytokines; oxLDL significantly reduced cell proliferation but had less negative impact on differentiation characteristics.

This study found that overexpression of miR-214 in keratinocytes inhibits hair follicle development and cycling by targeting β-catenin in the Wnt signaling pathway.

This study observed that in aged mouse skin, there was an increase in IL-17-expressing T helper cells, γδ T cells, and innate lymphoid cells, and blocking IL-17 signaling reduced skin inflammation and delayed age-related changes, suggesting it as a potential target to mitigate skin aging.