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This review highlights recent advancements in molecular understanding and treatment approaches for PCOS, including innovative drug delivery systems and AI-driven precision medicine, but provides no new clinical results.

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

This discussion provides a historical overview of modern hair restoration surgery and proposes a standardized graft classification system, but reports no new clinical results.

This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

This study proposes a new method for hair segmentation that improved performance in skin lesion images, as indicated by an increase in the Dice score from 76.97% to 79.08%.

This study identified specific morphological characteristics and kinship relationships among 27 candlenut genotypes in Karo Regency, North Sumatra, highlighting variations in stem, leaf, flower, fruit, and kernel traits, with the closest relationship between MTB1 and MTB2 genotypes.

This study introduced a novel framework that significantly improves the accuracy of alopecia areata lesion segmentation in semi-supervised scenarios, outperforming existing methods and aiding in the disease's diagnosis, treatment, and staging, which can impact quality of life and mental well-being.

This study suggests that elevated DKK1 expression, influenced by promoter methylation, is a significant prognostic biomarker for patients with head and neck squamous cell carcinoma.

Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.

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 study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.

This study used artificial neural networks to predict hair loss by analyzing factors like gender and zinc deficiency, suggesting neural networks may effectively model hair loss prediction.

This study reports that the monoclonal antibody BKN-1 specifically stained basal cell epithelioma cells and certain normal skin structures, indicating a similarity in keratin expression between the tumor and follicular epithelium below the isthmus portion.

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.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This study found that specific SNPs in the CXCL1 and CXCL2 genes may be associated with increased susceptibility to alopecia areata in the Korean population.

This study proposes a new mathematical model, the Harmonic Mean equation, for precisely quantifying nuclear pleomorphism in breast cancer grading, showing high performance with accuracy, recall, specificity, precision, and F1-score metrics.

This study reports that the recombinant expression vector pcDNA3.1-KK demonstrates specific expression in the skin of newborn mice.

Nephronectin is linked to worse outcomes in breast cancer and helps cancer spread.

This study introduces scINSIGHT, a method that showed improved performance over existing approaches in identifying gene expression patterns and cellular processes in heterogeneous scRNA-seq datasets from different biological conditions.

In this study, researchers created a detailed human skin cell atlas by analyzing over 700,000 cells, finding that disrupted communication among specific immune and stromal cell subsets may play a key role in initiating and sustaining chronic skin inflammation in atopic dermatitis.

This review outlines the current state of stem cell therapy in dermatology, highlighting its potential for treating a variety of skin disorders and conditions, while emphasizing the need for further controlled studies to standardize treatment and fully understand its efficacy and limitations.

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.

Certain NK cell changes in blood may indicate alopecia areata progression.

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 using the KNU implanter for follicular unit transplantation resulted in a high survival rate of 92% at 6 months, regardless of whether one-hair or two-hair units were used.

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.

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 study evaluated various NLP models for detecting bias in medical curricula, finding that fine-tuned BERT models perform well, whereas LLMs, despite being state-of-the-art in many tasks, are unsuitable for this application.