Analysis of Trichoscopic Images Using Deep Neural Networks for the Diagnosis and Activity Assessment of Alopecia Areata – A Retrospective Study

This consensus statement outlines a treatment algorithm for alopecia areata, detailing systemic treatment indications and options, including EMA-approved medications baricitinib and ritlecitinib for severe cases, as well as other off-label treatments and adjuvant therapies like oral minoxidil.

This review identified that baricitinib increases both short- and long-term hair regrowth in patients with alopecia areata compared to placebo, but found inconclusive results for serious adverse effects, while evidence for other treatments remains very uncertain.

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.

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

This study found that using planimetric surface area measurement might be a useful supplementary method to improve the accuracy of SALT scoring in alopecia areata, particularly for scores S1 to S3.

This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.

This study reviewed 39 studies with 3204 patients to identify characteristic trichoscopic findings in alopecia areata, highlighting yellow and black dots, broken, short vellus, and tapering hairs as key indicators. It found no single diagnostic trichoscopic finding but observed trichoscopy's utility in monitoring treatment response.

This study found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

This review summarizes trichoscopic features for diagnosing common hair loss disorders and reports no new clinical results, emphasizing their diagnostic utility in conditions like alopecia areata and trichotillomania.

This study found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

This study found that the HairComb algorithm achieved high accuracy in quantifying percentage hair loss across various types of alopecia, suggesting its potential for standardized automated assessments.

This study reviews the use of trichoscopy in diagnosing and monitoring alopecia areata, highlighting that certain trichoscopic findings like yellow and black dots, exclamation mark hairs, and others, provide valuable insights into disease status and treatment response.

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

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.

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 addresses the complexities in trichoscopy terminology and proposes an updated diagnostic flowchart for hair diseases, but it reports no new clinical findings.

JAK inhibitors show promise for treating alopecia areata, but more research is needed.

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

This article reviews the classification, diagnosis, and management of hair disorders, emphasizing the role of trichoscopy and early diagnosis for effective treatment but reports no new clinical findings.

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

This review reports no new clinical results but discusses fibrosing alopecia in a pattern distribution as a distinct scarring alopecia with characteristics of both androgenetic alopecia and lichen planopilaris.

This study found that in patients with patchy alopecia areata, certain trichoscopic features like upright regrowing hairs and pigtail hairs are positive predictive markers for treatment success.

This article discusses various aspects of trichoscopy for diagnosing hair and scalp disorders and reports no clinical results, offering guidance and highlighting pitfalls for better application of this tool by dermatologists.

This Viewpoint discusses the utility of dermoscope in routine practice and how familiarity with dynamic vs static trichoscopy may address its limitations, but it reports no new clinical findings.

This review presents various trichoscopic findings for diagnosing alopecia areata but reports no new clinical results, emphasizing that no single marker is pathognomonic and multiple features should be considered together.

This review explores nail changes associated with alopecia areata, noting their prevalence and impact on appearance and function, but reports no new clinical findings; the authors call for larger controlled trials.

This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.

This review discusses recent advances in trichoscopy for differentiating types of alopecia and presents a systematic approach for using its diagnostic findings, but reports no new clinical results.

This article reviews the use of trichoscopy for diagnosing common hair and scalp diseases and introduces a new classification for specific skin surface abnormalities, without presenting new clinical results.

This review discusses the dermoscopic features of common hair and scalp disorders and reports no new clinical results, aiming to assist dermatologists in diagnosing conditions like tinea capitis and alopecia areata.

This study describes characteristic trichoscopic features of various hair loss diseases and proposes an algorithmic method for diagnosing them, but reports no new clinical results.

This study reports that the TrichoScan method effectively measures hair growth parameters and detected significant improvements in hair counts and thickness in men with androgenetic alopecia after finasteride treatment.