Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

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 article reviews books on adolescent clinical assessment, body image disturbance, and grieving, but reports no new research findings.

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 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 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 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 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 thesis presents research conducted in partial fulfillment of a Master's degree in Software Systems Engineering but does not report new empirical findings.

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.

This study introduced a new high-throughput method for analyzing scalp hair morphology and found that quantifying hair form provides more accurate information than traditional classification based on racial categories, challenging the belief that cross-sectional morphology predicts hair curvature.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

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.

This study investigated the identity of Victor Vinnetou as Mbuyisa Makhubu using forensic facial comparison and DNA testing, but the findings were inconclusive, requiring further investigation.

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.

In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

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

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This study found that examining hair morphology provides deeper insights than classification, suggesting a potential population stratification artefact between hair curvature and cross-sectional shapes in the examined admixed African-European sample.

This article describes the Frontiers journal series, highlighting its open-access approach and tiered publishing system, but it presents no new empirical findings.

This thesis explores how optical coherence tomography and advanced imaging methods may address clinical needs in fields like interventional pulmonology and dermatology, highlighting their potential for disease assessment and staging.

In this study, researchers identified 32 differentially expressed genes involved in androgenetic alopecia, with down-regulated genes associated with Wnt and TGF-beta signaling and up-regulated genes linked to oxidative stress pathways.

This study demonstrated that CD19-CAR T cell therapy may promote structural regeneration in the skin of systemic sclerosis patients, as evidenced by histological improvements and fibroblast population changes, suggesting its potential for tissue remodeling in fibrotic diseases.

This study found that dedifferentiated corneal epithelial cells can revert to a stem-cell-like state, aiding tissue homeostasis and repair, with this plasticity limited to the epithelial lineage and enhanced by niche-derived cytokines.

This book reviews the symptoms, diagnosis, and treatments for Polycystic Ovary Syndrome, providing a comprehensive exploration but reports no new clinical results.

This study found that applying a keratin solution improved tensile strength, reduced absorbance, and increased gloss in damaged hair compared to untreated hair.

In this study, three patients with extensive alopecia areata showed varying degrees of hair regrowth after 20 weeks of treatment with ustekinumab, a cytokine-targeting therapy.

This study identified a crucial Gli2-Serpinh1 regulatory axis that regulates fibroblast state transitions during skin wound healing, shedding light on fibroblast heterogeneity and suggesting potential precision regenerative therapies.

This study found that VPS13C variants are significantly enriched in patients with idiopathic REM sleep behavior disorder (iRBD), associating these variants with more severe symptoms, autonomic dysfunction, and faster progression from iRBD to overt α-synucleinopathy in the iRBD-first disease subtype.