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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.

Scale created to measure hair loss in men and women; MK-386 reduces acne; Niaspan treats dyslipidemia; minoxidil increases heart rate.

This study found that a pre-trained image processing technique accurately classified scalp conditions with 85% accuracy, suggesting potential for automated diagnosis and treatment selection.

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.

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.

In this study, researchers developed a novel image processing method using a multi-class support vector machine that achieved an 89.3% accuracy in classifying alopecia areata and related conditions, outperforming existing models in classification accuracy.

EfficientNet improves accuracy in diagnosing hair loss stages.

This study concluded that the Scalp Photographic Index is a reliable and validated tool for objectively classifying and scoring various scalp conditions.

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.

In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.

This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.

In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.

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 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 dissecting folliculitis is more common than previously thought in Taiwan, with obesity possibly linked to higher severity of this condition, but smoking not associated.

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

Different factors help diagnose and treat hair loss accurately.

This study classified scars in discoid lupus erythematosus patients into six types based on anatomical location and morphology, suggesting early identification might influence more aggressive treatment strategies.

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.

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.

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.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

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

The document concludes that using a phototrichogram with a protractor and tapeline is a reliable and noninvasive way to measure hair loss.

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.

This review describes an improved scalp reduction technique that removes two to three times more bald skin than previous methods, utilizing wide undermining, serial relaxing incisions, and adrenocorticosteroids.

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.

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 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.