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This article reviews various methods and introduces new imaging technologies to assess the effects of hair shaping treatments on hair chemistry and structural integrity, without reporting clinical results.

This study found that laboratory-formulated natural shampoos condition hair better than commercial herbal shampoos, as shown by SEM micrograph analysis of hair microstructure.

This study found that Mueller Matrix microscopy can accurately identify, detect, and evaluate hair follicles in mouse skin tissue, suggesting its potential for skin structure research and dermatological applications.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study found that a lightweight Convolutional Neural Network model can accurately and quickly determine natural hair tone from high-resolution images of hair roots, outperforming other popular methods.

This study found that a hair tonic increased hair growth and reduced resting hair ratios in men with alopecia.

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.

This study validated that both tape-based and image-based methods for measuring head crown areas, and the CASLite NOVA as well as Image-Pro software for hair count, showed acceptable agreement, supporting their interchangeable use in hair care research settings.

This study utilized U-Net's image-processing capabilities to achieve 92% accuracy in segmenting individual hair strands, enhancing early detection and reliable identification of hair fall areas, which assists in addressing challenges of subtle hair thinning that are difficult to see otherwise.

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.

This research explored the impact of anticancer drugs on the scalp and found significant differences in scalp texture and features associated with alopecia, suggesting potential for assessing scalp morphology and changes during the hair regrowth process through image analysis.

This study found that low image resolutions allow expert clinicians to detect alopecia, but higher resolutions are necessary for identifying scarring and vellus hair, which may inform future image processing algorithms in dermatology.

Hair shaft malformation contributes to Central Centrifugal Cicatricial Alopecia.

This study found that androgenetic alopecia causes significant psychological distress, particularly in women, who exhibit a more negative body image and less adaptive functioning compared to controls.

Hair loss can cause low self-esteem, anxiety, and depression in men.

This review discusses the psychological and social impacts of androgenetic alopecia and suggests that medical treatments can have some positive psychological effects, highlighting the condition's moderate stress and impact on body image.

This review discusses the term "skin pore," its different meanings, and current methods to objectively describe skin pores, but reports no new clinical findings.

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 review discusses various methods for measuring scalp hair growth in androgenetic alopecia and reports no new results; the authors suggest that computer-assisted technology has untapped potential.

This study found that the Cutaneous Lupus Disease Area and Severity Index (CLASI) is correlated with both physician-assessed and patient-reported outcomes in cutaneous lupus erythematosus, particularly highlighting concerns about body image in visible areas.

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.

TrichoScan accurately measures hair growth and loss, showing increased hair counts and thickness with treatment.

This study found that dermatologists in São Paulo reported increased confidence in teledermatology over one year, citing its utility for triage and diagnosis, particularly for superficial infections and pigmentary disorders.

This review discusses the unmet supportive care needs of men and women experiencing chemotherapy-induced alopecia and reports no new results, urging healthcare providers to tailor interventions to individual patient needs.

This study found that the revised 43-item PCOSQ had better validity and reliability than the original version, although further studies are needed to assess its predictive efficacy.

This study found that photoacoustic imaging can effectively measure follicle density and angles, even when follicles are not visible to the eye, providing a promising alternative to traditional photographic examination.

This study developed a high-resolution phototrichogram system that can automatically and accurately assess hair growth metrics in cosmetic trials, achieving over 90% correlation with manual measurements.

This article discusses aspects of evaluating patients with androgenetic alopecia for hair transplantation and reports no clinical results.

This paper explores how hair functions as a geographical lens to redefine the body's borders, influencing perceptions of identity and power.

This study found that Turkish women with female pattern hair loss had significantly lower mid-scalp hair density and anagen ratio compared to healthy controls, with digital image analysis underestimating hair density.