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In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

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.

This study introduces ALUDWIG, an automated tool for assessing female androgenic alopecia severity from smartphone images, which may offer a more consistent alternative to current scoring methods like the Ludwig scale.

In this study, researchers developed a method to create a synthetic dataset of facial acne images using generative techniques, achieving 97.6% classification accuracy with InceptionResNetv2, which helps overcome privacy concerns in biomedical applications by using anonymized data.

This study found that applying transfer learning with CNN architectures like AlexNet, VGG16, and ResNet50 achieved 99% accuracy in classifying multiclass hair disorders, suggesting a potential technological aid for dermatologists in diagnosing and treating hair conditions.

This study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.

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 study used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.

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 convocation reviewed familiar topics in hair restoration surgery, offering no new findings.

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 used machine learning models, such as Convolutional Neural Networks (CNN), to accurately differentiate False Daisy from similar plants like Smooth Joyweed.

In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

This study observed abnormal extracellular matrix patterns in hair follicles from alopecia areata patients, particularly in the dermal papilla of large anagen follicles and in catagen follicles from lesional sites.

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.

EfficientNet improves accuracy in diagnosing hair loss stages.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

This study identifies a mechanism where dsRNA activates TLR3 to induce RA production, promoting hair follicle regeneration in mice and suggesting a potential role in human tissue regeneration.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

This narrative review found that while AI-based tools in dermatology, particularly for hair disorder assessment, have potential to enhance clinical practice by improving objectivity and personalization, they currently serve mainly a complementary role and face challenges like methodological limitations and data bias.

This study suggests that edelweiss extract may promote hair growth and reduce shedding in ex vivo experiments by affecting hair follicle anagen phase duration and keratinocyte proliferation.

This study reported that a treatment serum containing ingredient blends with panthenol, licorice extract, and red clover extract significantly improved hair volume, scalp coverage, and reduced hair breakage over 6 months in individuals with thinning or damaged hair.

This study found that oral spironolactone improved or maintained hair condition in females with scarring alopecia and female pattern hair loss, with 83% of patients tolerating the treatment well, despite some side effects like hyperkalemia and leg cramps.

The study presents a case report of a 17-year-old male with rapidly progressive alopecia areata that worsened following emotional stress; the authors reported complete hair regrowth after treatment with individualized homeopathic prescribing, nutritional correction, and lifestyle guidance at Dr Batra’s® Homeopathy Clinic.

This case report observed improvements in both hair regrowth and psychological symptoms in a 25-year-old male with alopecia areata after six months of individualized homeopathic treatment, suggesting potential benefits of this approach for such patients.

This case report details the significant hair regrowth experienced by a 49-year-old woman with stress-related alopecia totalis after individualized homeopathic treatment, underscoring its potential effectiveness in managing both emotional trauma and hair loss.