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This letter compares automated digital image analysis (TrichoScan) with manual marking of hairs in male patients with androgenetic alopecia but reports no new results.

This study found that high-frequency ultrasonography may effectively distinguish different stages of alopecia areata and differentiate it from other scalp conditions.

This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

A deep learning model accurately detects early hair loss signs using scalp images.

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

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 article discusses the presence of dirty dots as a normal trichoscopic finding in children, noting they are not observed in adults or other age groups, and reports no new results.

The authors concluded that trichoscopic examination can be useful for diagnosing syphilitic alopecia in patients with idiopathic alopecia, though the findings are nonspecific and further studies are needed.

This article discusses the utility of trichoscopy in distinguishing hair disorders with similar clinical appearances but reports no new clinical results.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

This study identified three genetic loci influencing hair density in East Asian populations and found associations with demographic and lifestyle factors like age, sex, and BMI. The results also suggest possible genotype-specific responses to finasteride for managing hair disorders.

This study reviewed trichoscopic images to assess hair loss, finding that ayurvedic signs (lakshanas) aligned with modern trichoscopic parameters, potentially aiding accurate diagnosis and management of hair conditions.

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 abstract presents supplementary trichoscopy images for alopecia areata and scarring alopecia but reports no clinical findings.

This study developed new graded visual scales integrating macroscopic and trichoscopic images for assessing the severity of androgenetic alopecia in men and women, potentially aiding in more objective disease evaluation.

This study found that Follicular Maps, derived from trichoscopic images, remained consistent over time and unaffected by hair cycling or noncicatricial alopecia, offering a precise tool for diagnosing and monitoring hair and scalp conditions.

This study reports that publicly available large language models are currently less accurate than human experts in diagnosing trichoscopic images, suggesting the need for further development and specialized training for these AI tools in trichology.

This study associated ivory-colored areas observed through trichoscopy with steroid deposits in the dermis among patients with steroid-induced atrophy on the scalp.

This study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.

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.

In this study, telemedicine using trichoscopy for alopecia areata patients showed high concordance with outpatient trichoscopy in image quality and similar levels of patient satisfaction, indicating its effectiveness for follow-up and continuity of care.

In this study, researchers investigated the alignment of trichoscopic findings with ayurvedic signs (lakshanas) for diagnosing hair loss, suggesting that integrating these perspectives could enhance diagnostics and management of hair and scalp conditions.

This study found that certain trichoscopic features, including hair diameter diversity patterns, helped differentiate female androgenetic alopecia from telogen effluvium in women, suggesting their potential use in clinical diagnosis.

This study found that trichoscopy is an effective method for monitoring treatment response in androgenetic alopecia when conducted by doctors experienced in hair diseases, showing excellent agreement with TrichoScan measurements for various hair parameters.

A new tool helps better assess and treat hair loss in Chinese men.

This chapter reviews trichoscopic criteria and diagnostic features for various hair and scalp conditions, emphasizing their utility in non-invasive diagnosis, but reports no new clinical results.