95 citations
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January 2007 in “Human biology” This study proposes a new, objective method to classify human hair into eight categories based on specific shape measurements, rather than traditional ethnicity-based classifications.
16 citations
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June 2017 in “PLoS ONE” This small study reports that a 6-group geometric classification method is more reliable for classifying human hair curl, although a digital system could further reduce errors.
May 2020 in “Hair transplant forum international” 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.
July 2007 in “Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature” 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.
3 citations
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October 2017 in “Journal of Cosmetic Dermatology” This study presents a new classification of adult human scalp hair patterns based on high-resolution photographs, which may assist hair restoration surgeons and dermatologists in determining appropriate punch size for follicular unit extraction.
125 citations
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May 2007 in “Journal of The American Academy of Dermatology” This study describes the development of the BASP classification system, providing a universal and systematic approach for classifying pattern hair loss in both men and women.
1 citations
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January 2023 in “Annals of Dermatology” The BASP classification helps predict patient behavior and improve treatment for hair loss.
EfficientNet improves accuracy in diagnosing hair loss stages.
May 2021 in “Pakistan Journal of Medical and Health Sciences” This study found that among Pakistani men with hair loss, the M type pattern was the most prevalent according to BASP classification.
8 citations
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November 2022 in “International Journal of Cosmetic Science” This review discusses the limitations of current hair classification systems and highlights the need for standardized reporting of key hair characteristics to improve study comparisons; it reports no new results.
138 citations
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February 2007 in “European journal of cancer” This review discusses the classification, pathogenesis, and management of skin, hair, nail, and mucosal changes due to EGFR inhibitors, emphasizing the importance of informing patients to improve therapy compliance.
27 citations
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January 1983 in “Journal of the American Academy of Dermatology” This article proposes a classification system for hair casts to resolve confusion in existing literature and describes a staining technique to distinguish peripilar keratin casts from other hair disorders, without providing new clinical results.
16 citations
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October 2012 in “The Journal of Dermatology” This study found that the BASP classification system for pattern hair loss showed better reproducibility and repeatability compared to the Norwood-Hamilton classification.
8 citations
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January 2013 in “International Journal of Trichology” This study found that the BASP classification is an effective and easily remembered method for diagnosing and treating male and female pattern hair loss in the Indian population.
4 citations
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October 2022 in “Journal of Imaging” This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.
2 citations
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June 2019 in “International Journal of Dermatology” This study found that while a modified BASP classification for pattern hair loss could classify previously unrecognized types, it was less accurate and harder to use than the existing BASP classification.
1 citations
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November 2024 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.
This study developed an automated image analysis framework for diagnosing hair disorders using trichoscopic images, reporting a Random Forest classifier as having an 86.67% accuracy in distinguishing between different scalp pathologies based on quantitative image features.
Hair restoration surgery is becoming more common as male pattern baldness increases with age.
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.
In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.
October 2023 in “Sinkron” This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.
April 2012 in “Informa Healthcare eBooks” Classifying hair diseases, like alopecia, is difficult and needs more research to understand their causes.
18 citations
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July 2003 in “Dermatologic Surgery” This study identified five distinct scalp whorl patterns, noting that white males have the most distinct whorls, while African Americans and women more often exhibit a diffusion pattern.
3 citations
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January 2018 in “International Journal of Trichology” The authors concluded that their new grading system can effectively classify early female pattern hair loss and evaluate treatment progress.
September 2023 in “Pakistan Journal of Medical & Health Sciences” This study found that among male hair transplant patients, most had a single crown hair whorl pattern, which was more often located centrally on the scalp compared to dual or undetectable patterns.
January 2022 in “Journal of Pharmaceutical Negative Results” This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.
January 2016 in “프로그램북(구 초록집)” 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.
August 2003 in “Dermatologic Surgery” This study identified five distinct natural scalp whorl patterns and noted that White males have more distinct whorls, whereas African Americans and women often display a diffusion pattern.
October 2022 in “Hair Transplantation” This review discusses genetic factors and associated diseases linked to androgenetic alopecia but reports no new clinical findings.