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This study introduced a new classification method for male pattern baldness in Korean men, detailing six types that could guide hair restoration surgery and further research.

This article presents a classification system for stages of female-pattern androgenetic alopecia to aid early diagnosis and mentions existing systems for rare male-pattern cases, but reports no new clinical results.

This letter to the editor discusses the prevalence of frontal-vertex baldness and its association with family history in Korean men but reports no new findings.

This article proposes enhancing the Norwood-Hamilton scale for assessing hair loss patterns but reports no new clinical results.

This article memorializes Dr. O'Tar T. Norwood and highlights his contributions to the field of hair restoration surgery; it does not report new research findings.

This study found that the BASP classification system for pattern hair loss showed better reproducibility and repeatability compared to the Norwood-Hamilton classification.

This study suggests that male pattern baldness—especially moderate to severe—is an independent risk factor for prostate cancer and high-grade disease in men undergoing prostate biopsy.

In this prospective cohort study, male pattern baldness was identified as an independent predictor of prostate cancer, while the 2D:4D finger ratio was not associated with the cancer risk.

This study found no difference in male pattern baldness between patients with benign prostate hyperplasia and prostate cancer, and no correlation with serum androgen levels.

Some men can have female pattern baldness without hormonal abnormalities or signs of feminization.

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

EfficientNet improves accuracy in diagnosing hair loss stages.

In this study, a deep learning model using an optimized VGG19 architecture achieved a high classification accuracy of 98.64% for detecting ten hair disease classes from a balanced dataset, indicating its potential for reliable use in mobile diagnostics for clinical and remote applications.

This study found that the revised BASP classification for pattern hair loss, which addresses certain limitations of the original, could serve as an alternative option despite a decrease in clinical accuracy and ease of use.

This study introduced the BASP classification, a new system for categorizing pattern hair loss in both men and women, and applied it to analyze 2213 Korean subjects.

Familial factors affect hair loss types in Koreans, with M type in men, L type in women, and paternal factors influencing male hair loss more.

A trial found that using finasteride, a hair growth drug, can be effectively measured by comparing before and after photos.

This review discusses the classification of female androgenic alopecia using the Ludwig classification and details the hair growth cycle but provides no new clinical results.

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 paper introduces the first computational model of male baldness that simulates hair loss using the Hamilton-Norwood classification and has been deemed plausible for clinical use by a dermatologist.

This article introduces a revised NPRT classification system that includes diverse patterns of hair loss in both men and women, suggesting it as a universal tool for classification and documentation.

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

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 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 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.

This study, involving an international panel of 16 psychodermatology experts, proposed a new classification system for psychodermatology disorders that aims to unify previous classifications and improve their management by systematizing disorders into two main categories: primary mental health disorders and primary skin disorders.

Hair restoration surgery is becoming more common as male pattern baldness increases with age.

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 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 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.