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This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

This study found that high-frequency ultrasonography may effectively distinguish different stages of alopecia areata and differentiate it from other scalp conditions.

This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

This study developed a method using Convolutional Neural Networks to detect nutritional deficiencies, such as iron, zinc, biotin, and vitamins, through high-resolution images of hair and nails, achieving an 89% accuracy rate in identifying these deficiencies.

A deep learning model accurately detects early hair loss signs using scalp images.

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

This study illustrates that low-dose oral minoxidil shows clinically notable hair regrowth, particularly at the crown in male patients, with differing efficacy from bitemporal sites; images document regrowth in a male and a female patient.

This study concluded that TrichoScan is error-prone in its current form and overestimates certain hair growth parameters, with results not aligning with clinical severity of alopecia.

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

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 article discusses the pervasive presence and influence of advertisements in daily life but does not provide new research findings.

This study found that men receiving negative appearance-related commentary are more likely to experience negative psychological outcomes, while positive feedback tends to lead to better outcomes despite encouraging appearance-change behaviors.

This study finds that the images of soft-haired Kerr black holes show unique rotational, dilational, and drifting effects compared to bald black holes, linked to Noether charges and generalized symmetries.

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 developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

This study found that women with PCOS in a Middle Eastern population reported significantly lower self-esteem and body image satisfaction compared to those without PCOS, with PCOS respondents feeling less attractive, avoiding mirrors and social interactions, and expressing a stronger desire to lose weight.

This article reviews how SALT scores are used in clinical trials for alopecia areata and provides patient images to help clinicians understand and apply these scores in practice; it reports no new clinical results.

This study found that skin computed tomography may effectively identify epidermoid cysts by revealing features consistent with histopathology, potentially serving as a non-invasive diagnostic alternative to biopsies.

Advanced imaging techniques are crucial for accurately diagnosing Monilethrix, a rare hair disorder.

This study used phase contrast hard x-ray microscopy to capture detailed images of healthy human hair, revealing distinct structures such as the medulla, cortex, and cuticular layer.

This study demonstrated that current facial recognition software can accurately identify individuals from MRI head scans within minutes, highlighting significant privacy concerns for clinical trial participants and the need for enhanced data protection measures.

This review discusses the potential benefits of resistance exercise and art therapy for improving body image in breast cancer survivors, highlighting a need for more theory-informed research.

This study found that women recovering from cancer developed a positive body image by focusing on acceptance and newfound trust in their bodies, despite the trauma of illness.

This study found that varied hair colors influence body image by creating different perceptions, such as charisma and friendliness, with specific colors like red and black producing strong image effects.

In this study, aesthetic surgery significantly improved body image and psychological health; however, high preoperative body image distress predicted lower satisfaction post-surgery, highlighting the importance of psychological screening to manage expectations and outcomes.

This study found that self-supervised pretraining significantly improves accuracy in medical image classifiers for dermatology and chest X-ray tasks, outperforming supervised baselines and showing robustness to distribution shifts with limited labeled data.

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