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This review summarizes evidence indicating that body image issues in patients with systemic lupus erythematosus should be addressed with the same importance as physical symptoms like pain and disability.

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.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

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 article describes the immunofluorescence tomography method to achieve high-resolution 3-D reconstruction of epithelial tissues and reports no clinical results.

This study found that in Japanese, German, and American females, changes in hair lipid patterns with age may reflect alterations in sebum excretion and affect hair texture.

This study found that their proposed method for facial image restoration using Denoising Diffusion Probabilistic Models produced higher-quality results compared to traditional methods, particularly improving face recognition accuracy with different types of masks.

This study observed phenotypic diversity in hair loss among Japanese individuals homozygous for the LIPH c.736T>A mutation and suggests that differences in hair thickness may contribute to varying severities.

In this research, experienced clinicians noted inconsistency in the reproducibility of the commonly used Hamilton-Ludwig scales for assessing pattern hair loss severity in photographic assessments, leading to the proposal of a new 5-point scale for staging female hair loss.

In this study, improved hidden Markov algorithms based on Bayesian methods enhanced the resolution and segmentation accuracy of low-dose CT images significantly more than naive Bayesian methods.

This study introduced a semi-automatic hair follicle implanter that can reduce surgery time and fatigue for both patients and surgeons, with effectiveness demonstrated in animal and clinical trials.

This study describes a simple, noninvasive hair counting method used in a hair growth prevention trial, assessing its reliability and validity.

This study found that GPC1 is a key regulator of angiogenesis in hair follicles and may be an interesting target for addressing alopecia in dermatology research.

This study found that GPC1 plays a crucial role in regulating angiogenesis in human dermal microvascular endothelial cells, which may make it a potential target in alopecia treatment research.

In this study, the researchers used a human hair follicle model to demonstrate that T cell stimulation can induce an alopecia areata–like phenotype, with cell proliferation and immune privilege collapse; however, the DHODH inhibitor farudodstat partially mitigated these effects by reducing T cell proliferation.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

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 review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

Certain products and treatments can improve hair health and growth.

This study found that the occipital scalp had significantly higher epidermal nerve fiber density than the frontoparietal scalp, which may explain increased sensitivity in some patients.

This study demonstrated that an RO formulation significantly improved neck wrinkles after 8 weeks of application, compared to a placebo, in Japanese female subjects.

Photographic imaging is crucial for documenting and managing hair loss, requiring careful preparation and standardization to be effective.

This study reported that treating wounds with a hydrogel from acellular porcine adipose tissue and adipose-derived stem cells may significantly enhance wound healing and regeneration by promoting new adipocyte formation.

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 study reports that the TrichoScan method effectively measures hair growth parameters and detected significant improvements in hair counts and thickness in men with androgenetic alopecia after finasteride treatment.

This mini-review summarizes the Trichoscan as a sensitive tool for measuring hair growth parameters, demonstrating its effectiveness in detecting treatment response in androgenetic alopecia but noting some practical limitations.

This study found that body image-related quality of life significantly mediates the relationship between pain and depressive symptoms in patients with systemic lupus erythematosus.

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 article discusses autoimmune-associated alopecia areata, highlighting its psychological and social impact, but reports no new clinical findings.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.