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In this study, researchers identified two person-centered sexual function profiles among women, linked to physical and psychological factors, with PCOS showing greater, though not significant, presence in the dysfunction profile. The dysfunction was associated with higher adiposity and body-image distress.

This study found that DALL-E 2 performed poorly in generating accurate images of most pediatric dermatological conditions, highlighting the need for more domain-specific and inclusive training data.

In this study, a computer-aided imaging system accurately measured the width of balding areas in female pattern hair loss, correlating strongly with clinical staging severity.

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.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

This study developed a method to quantify hair loss in mice, aiding the evaluation of new treatments for alopecia.

In this study, Scalp Immersion Proxigraphy (SIP) was shown to produce more accurate measurements of linear hair growth rate than crude phototrichogram techniques, suggesting SIP's suitability for clinical trials monitoring hair growth.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

In this study, researchers developed a method to automatically detect androgen receptor nuclear translocation in dermal papilla cells using fluorescence markers and image analysis, finding that the receptor's nuclear signal peaks about 20 minutes after DHT exposure.

This study found significant differences in hair density, non-vellus hair diameter, and percentage of miniaturized hair between normal women and those with female pattern hair loss, particularly in the midscalp and parietal areas.

This study outlines the Human Gene Correlation Analysis tool, which classifies human genes by coexpression levels and identifies overrepresented annotation terms in correlated gene groups, with no new clinical results reported.

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.

The study found that balding scalps have more thin hairs and larger oil glands, which might contribute to skin conditions related to hair loss.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

Dermoscopy is a good, noninvasive way to see normal cat skin structures and could be useful for check-ups.

AI can accurately measure hair loss severity in alopecia areata.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

This study introduces Cell Journey, a new platform for visualizing RNA velocity in 3D, which aims to better capture complex cellular transitions in single-cell datasets compared to current 2D methods.

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 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.

This study found that a hydro-alcoholic extract of Roselle petals may reduce free radical production during UV-induced photodecomposition of antibiotics, potentially offering protective benefits for patients using similar topical antibiotics.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

This study reviews AI's role in aesthetic medicine, noting it enhances diagnostic accuracy and personalized treatment planning, but faces challenges like ethical concerns, algorithmic biases, and regulatory issues that need addressing for successful integration.

This study developed an AI-driven method for classifying cells in Follicular Lymphoma cases, achieving a 63% F1-score, precision, and recall in distinguishing centroblasts from other cell types using whole slide images at x20 resolution.

MRI helps distinguish between pituitary adenomas and craniopharyngiomas, guides treatment for pediatric CNS tumors, and assesses rhinocerebral mucormycosis with a high mortality rate in transplanted patients.

This article reviews the endocrine changes in anorexia nervosa and bulimia nervosa, highlighting hypoleptinemia as a potential mediator, but does not present any new clinical results.

This study investigated the identity of Victor Vinnetou as Mbuyisa Makhubu using forensic facial comparison and DNA testing, but the findings were inconclusive, requiring further investigation.

This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.