Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

The researchers presented a case study on Exo.Reset®, a cosmetic formulation using extracellular vesicle technology, demonstrating systematic application of recognized standards to ensure scientific rigor and regulatory compliance in skin rejuvenation product development, providing a practical model for EV-based cosmetic innovations.

This study introduces "Homeodynamic Rejuvenation" as a method to restore the skin's resilience by enhancing its ability to detect and recover from stress, focusing on five core biological processes critical for maintaining skin function and health amidst environmental and metabolic challenges.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

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.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

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 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 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 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.

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

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 reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

In this study, researchers devised a stochastic hair growth model influenced by regular haircuts, using a process that undergoes linear growth subject to random resets, and used it to theoretically determine an ideal haircut routine.

This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

This study found that male and female chicken feather morphology and color patterns can be extrinsically modified through molting and resetting the stem cell niche during regeneration.

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.

This study found that while a tip-focused calcium gradient influences localized growth in root hairs and pollen tubes, it is not the primary determinant of growth direction in root hairs of Arabidopsis thaliana.

This review examines regenerative hair waves and complex hair cycle domains in transgenic mice, highlighting how these dynamic processes contribute to skin regeneration and the physiological regulation of organs, but reports no new experimental results.

This study describes how feather pigmentation in birds is regulated by melanocyte distribution, differentiation, and patterned agouti expression, contributing to diverse and adaptive color patterns.

This review discusses the interactions between melanocyte stem cells and their niche in hair regeneration and repair, but reports no new results; recent studies on molecular pathways are highlighted.

This study reported the establishment of a chemically defined culture system that supports tooth reconstitution and detailed how co-stimulation of specific signaling pathways can sustain tooth development stages and initiate enamel formation, providing insights into tooth regeneration mechanisms.

This study presents the design of an autonomous bioregeneration chamber that aims to extend human lifespan to 130-150 years by optimizing biological and environmental conditions, suggesting that a longer healthspan is possible through technological, nutritional, and socio-economic interventions.

This research outlines an innovative bioregeneration chamber aimed at significantly extending human lifespan and enhancing health maintenance by treating the body as a thermodynamic system, potentially enabling an average lifespan of 130 to 150 years in an advanced therapeutic setting.

In this study, the researchers observed that post-radiation hair follicle repair in 3D architecture occurs through independent, long-range cell movements along the basal surface, resembling 2D healing processes.

This study reports various methods used by hair transplant surgeons to count and manage follicular grafts during follicular unit extraction hair restoration, including manual counting and the use of automated or digitalized graft counters.