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In this study, R-CSLM showed promise in evaluating hair shaft diseases by providing high-quality images of hair structures, although further development is necessary for follicle and perifollicular area analysis.

This study discusses how beauty standards contribute to structural injustice by imposing rising demands and increasing body image anxiety across various demographics.

This case study suggests that thermography could objectively assess pain severity in herpes zoster patients by correlating thermal images with reported pain intensity.

This study observed that hair characteristics significantly impact daily life, self-image, and well-being, varying by gender, ethnicity, and country, suggesting a need for interventions to address these psychological and social effects.

This study found that Follicular Maps, derived from trichoscopic images, remained consistent over time and unaffected by hair cycling or noncicatricial alopecia, offering a precise tool for diagnosing and monitoring hair and scalp conditions.

The book is a useful guide for dermatologists studying for their board exam, with clinical images and features of skin diseases.

This study found that hyperpigmented skin changes resembling Schamberg’s purpura occur in areas affected by CRPS and can also appear on mirror-image locations of unaffected limbs, potentially improving with the condition's treatment.

This abstract reports no new research findings; it includes an educational image of microorganisms found in a skin biopsy.

In this study, researchers developed an AI-powered platform called VitaDetect, which screens for vitamin deficiencies using image analysis of nails, tongue, and skin, aiming to provide an accessible and early-stage detection tool in resource-limited settings.

This study found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

This study assessed Palestinian women newly diagnosed with breast cancer and found that higher spiritual well-being is associated with improved body image and sexual functioning, and reduced distress from treatment side effects, suggesting spiritual support should be integrated into cancer care.

This study introduces ALUDWIG, an automated tool for assessing female androgenic alopecia severity from smartphone images, which may offer a more consistent alternative to current scoring methods like the Ludwig scale.

This article presents a new method using dermoscopy to evaluate the severity and treatment effectiveness of androgenetic alopecia in men by capturing images of specific scalp regions.

In this pilot study, the CNC® device improved body image perceptions in patients with chemotherapy-induced alopecia but did not significantly affect psychological wellbeing; it may help prevent discomfort and enhance everyday life compared to traditional wigs.

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

This study concluded that estimating split thickness skin graft take by observation is as reliable as using the Image J digital program.

This meeting summary discusses the ISHR's focus on hair restoration, featuring Julius Caesar's image, but reports no new research findings.

In this study, a method using nano-sized iron particles successfully captured images of sub-dermal hair follicles, showing comparable size and shape to those removed from the skin.

This study found that vitiligo elicited the most compassion and curiosity among general adults viewing AI-generated images of chronic skin diseases, while psoriasis was linked to higher perceptions of disgust and blame.

This research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This review outlines key considerations for designing fluorescence microscopy experiments in cell biology, discussing the importance of hardware availability, suitability of biological models, and imaging agents to achieve high-resolution and informative images.

This study found that men appearing in photos after a hair transplant were perceived by observers as younger, more attractive, more successful, and more approachable compared to their pretransplant images.

This study found that alopecia significantly reduces quality of life among South African Black women, with subjective symptoms and self-image concerns having the greatest impact.

This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

This study among Indian patients who underwent cosmetic procedures found that these treatments generally improved their interpersonal relationships with family and peers, with a noted gender difference in perceptions of facial image.

In this study, telemedicine using trichoscopy for alopecia areata patients showed high concordance with outpatient trichoscopy in image quality and similar levels of patient satisfaction, indicating its effectiveness for follow-up and continuity of care.

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

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 review concludes that polycystic ovary syndrome can significantly impact aspects of health-related quality of life in adolescent girls, especially body image and interpersonal functioning.