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This chapter discusses common endocrine disorders affecting the pituitary, thyroid, and adrenal glands, and their impact on human fertility, emphasizing practical evaluation and treatment algorithms.

This review highlights the challenges and management strategies associated with persistent chemotherapy-induced alopecia, detailing the psychosocial impacts, follicular pathobiology, and available treatments such as scalp cooling and minoxidil, ultimately proposing a clinical algorithm to transform it into a preventable and manageable condition.

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 case report suggests the co-occurrence of systemic lupus erythematosus and an eating disorder in an adolescent may be linked through shared adolescent stress factors rather than neuropsychiatric lupus, despite algorithmic indications.

This abstract highlights the prevalence of androgenetic alopecia in men and young adults and mentions the psychological impact, noting that while various therapies exist, clear treatment algorithms are lacking according to this source.

In this study, researchers at the Laboratory of Regenerative Medicine WUM are exploring the long-term effects of SARS-CoV-19 infection, focusing on stem cell mobilization and engraftment processes, and utilizing advanced diagnostic techniques to develop algorithms for rare disease classification, including amyloidosis.

This systematic review identified and evaluated the most accurate ICD code classification methods for dermatologic conditions in US-based datasets, highlighting both high-performing algorithms and areas lacking validation, thereby informing future research and dataset use.

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 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 outlined modern approaches for using cosmeceuticals in trichology, emphasizing evidence-based medicine, personalized care, and patient safety. It proposed the H.A.I.R. model as a universal algorithm for trichological programs and suggested new educational and practical tools for specialists in trichology and aesthetic medicine.

This paper explores the diverse patterns and social significance of Black hair, approaching it as an art form, mathematical pattern, and ethnomathematic entity, and seeks to dispel myths while highlighting its vast possibilities.

This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.

Up to 50% of scalp hair can be lost before it appears thin, and treatment is only needed for hair loss caused by diseases or deficiencies.

In this retrospective study, researchers developed machine learning models to predict mortality risk among 7115 COVID-19 patients in Iran, finding that the random forests model performed best with 96% accuracy and identified factors like intubation and SpO2 as significant predictors.

This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

This study suggests that stress intensity is highly correlated with hairfall severity, highlighting the potential of an inexpensive and accessible machine learning approach for forecasting and prevention.

This study describes a system called FOLLYSIS©, which uses mathematics and image analysis to optimize Follicular Unit Extraction hair transplants, showing high accuracy in measuring donor area density and reducing donor site injury.

This article reviews current and emerging treatments for alopecia areata, including Janus kinase inhibitors, highlighting variability in clinical outcomes and the lack of sustained remission.

This study found that about 80% of known fullerene-binding proteins rank in the top 10% of scorers for C60 docking sites, confirming the accuracy of the predictive model used.

This study confirms the presence of large structural variations in the genome of Nellore cattle, which may contribute to their environmental adaptation to tropical regions.

This article examines the emerging role of dermoscopy in skin cancer diagnosis and reports no new clinical findings; the authors highlight its potential to enhance dermatological practice.

This study developed a novel multi-scale image descriptor using dictionaries for classifying histological images, achieving average recall and precision measures of 0.81 and 0.86 in identifying specific skin structures and pathologies.

This study demonstrated that the Alopecia Areata Assessment Tool (ALTO) effectively identifies alopecia areata cases with high sensitivity and specificity in a dermatology clinic setting.

This article reviews infertility diagnosis and initial management for advanced practice clinicians, emphasizing the role of midwives and advanced practice nurses, but reports no new clinical findings.

This study found that in men with high genetic risk factors for polycystic ovary syndrome, there was an associated increase in obesity, type 2 diabetes, coronary artery disease, and androgenic conditions, suggesting these genetic factors can act independently of ovarian function.

This study found that anagen stage protein homogenates and specific epitopes from melanogenesis proteins activated CD8 T cells, suggesting alopecia areata is an anagen-specific disease.

This study highlights the importance of recognizing non-nutritional forms of rickets, which can manifest with subtle symptoms like alopecia and cataracts, and emphasizes that a comprehensive diagnostic approach, including genetic testing, can improve management and treatment outcomes.

This study reports that a clinical prognostic model based on immune-related genes improved survival prediction for patients with clear cell renal cell carcinoma, also identifying potential drugs targeting the gene DOCK8.

In this systematic review, the researchers provide an overview of recent techniques and innovations in the surgical reconstruction of traumatic scalp injuries, highlighting various methods and factors influencing the choice of reconstructive strategy to improve patient outcomes.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.