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This review discusses various classification systems for patterned hair loss in both sexes and reports no new clinical findings, highlighting the Hamilton-Norwood and Ludwig systems as the most commonly used.

This review discusses various classification systems for male-pattern hair loss, comparing their detail, practicality, and reproducibility, and reports no new clinical outcomes.

This study suggests that traditional classification systems for pattern hair loss, while useful in the past, have limitations in accuracy and reproducibility, and highlights the potential of integrating digital imaging and AI to create more precise and biologically informed classification frameworks.

This article discusses the use of the Hamilton and Norwood system by hair restoration surgeons to classify stages of androgenetic alopecia and presents no new clinical results.

This article reviews the terminology and factors associated with female hair loss, particularly distinguishing it from male pattern hair loss, but reports no new clinical results.

This study developed an objective standard grading system for evaluating hair damage using scanning and transmission electron microscopy.

This study found that treatment modalities and patch size influence hair regrowth patterns in alopecia areata patches.

This study introduces the PRECISE scale, a quantitative tool to classify androgenetic alopecia, aiming to improve planning and outcomes for hair transplantation surgeries by evaluating the whole hairless and thinning areas.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This study investigated variation in depth control during follicular unit extraction and introduced a new depth classification, highlighting depth variability within different zones of the scalp's safe donor area.

This study found that an adapted version of the Hamilton-Norwood classification improves reliability in assessing pattern hair loss and could aid population studies examining its association with cardiovascular disease.

In this study, researchers introduced the Sanusi FUE Score Scale to better predict and grade the difficulty of follicular unit excision hair transplantation by accounting for diverse hair and skin types, with further validation needed.

This review discusses hair transplantation advances for female alopecia, offering a classification system and various techniques, while emphasizing preoperative evaluation and addressing the psychological impact and expectations of hair restoration in women.

In this review paper, the authors explored the composition and role of lipids in human hair, highlighting their importance for hair texture, moisture retention, and tensile strength, while noting that the understanding of hair lipids' functions remains controversial and requires further research.

This study observed that using the LMNN algorithm improved diagnostic accuracy in identifying biomarker correlations associated with hair loss, suggesting potential for advanced automated diagnostics.

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

This study found that human hair follicles demonstrated a dose-dependent response to light in an ex vivo photoepilation model, which could predict the clinical efficacy and permanency of light-based hair removal devices.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

This study examined the morphological variations of terminal hair from different body regions of a Caucasian male, finding notable differences in diameter and features like "steak-boning" in specific hair types.

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

This review analyzes existing literature on platelet-rich plasma for hair loss, discussing its mechanisms and preparation, but reports no new clinical results and highlights the inconsistency of current evidence.

This study found that temporal hair loss is evident in Korean women with female pattern hair loss, suggesting it should be included in classification systems like the BASP.

The Hair India 2010 conference introduced a new hair loss classification and highlighted advanced diagnostic techniques in trichology.

Different factors help diagnose and treat hair loss accurately.

This article discusses patient selection and procedural design for hair transplant surgery without reporting new clinical results; it also considers how hair transplant designs can align with various hairstyles.

This review discusses alopecia classifications for diagnosis and prognosis but reports no new clinical findings.

This study proposes a new clinical scoring method for hair density based on hair diameter diversity, which correlates with existing measures and reflects follicle miniaturization in androgenetic alopecia.

This review discusses the development of an algorithmic guideline for treating androgenetic alopecia in Asian patients, using the BASP classification to address limitations of previous guidelines, and reports no clinical results.

This study reported that long-pulse alexandrite laser treatment achieved an overall hair reduction rate of 80.8% among patients, with minimal cases of hypo- or hyperpigmentation.

Hair diameter diversity is a key sign for diagnosing and managing male pattern baldness.