Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

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.

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.

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.

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

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.

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 study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.

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 using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

This paper critically evaluates the role of AI in cosmetic surgery, highlighting its potential to enhance precision, tailor treatment, and improve patient outcomes, while also addressing ethical, legal, and regulatory challenges that complicate its integration into clinical practice.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

This review discusses recent research on exosomes' role in dermatology, highlighting their involvement in skin diseases and rejuvenation and exploring therapeutic possibilities in these areas.

This article provides a protocol for live imaging of C. elegans germline stem cells, highlighting their potential for studying mitosis and other cell processes, but does not report new experimental findings.

This study found that the SketchAndCalc tablet application offers an easy, rapid, and accurate method for measuring recipient areas in hair restoration, particularly excelling in accuracy for long and narrow shapes.

This study explored using 3D models derived from reflectance confocal microscopy to better understand and differentiate melanoma on sun-damaged skin, suggesting enhanced diagnostic possibilities.

This study presents a new method called scMC that effectively distinguishes biological from technical variation in single-cell genomics datasets, demonstrating its ability to accurately align and detect biological signals across various experiments.

Cone beam computed tomography can allow for smaller safety margins around the target area in prostate cancer radiation treatment if used for ongoing treatment checks.

This article explores the theoretical foundations of the ChromaLens Precision Mapping system for analyzing hair, but it does not present any new experimental findings.

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 outlines the Human Gene Correlation Analysis tool, which classifies human genes by coexpression levels and identifies overrepresented annotation terms in correlated gene groups, with no new clinical results reported.

This study demonstrates that a new Adaptive Modulation and Coding method can improve control performance in Communication-Based Train Control systems using WLAN by reducing average delay through effective transmission mode selection.

This study found that ChatGPT 4.0 and Gemini 1.5 Flash provided more accurate and user-friendly responses to androgenetic alopecia questions than Deepseek R1, suggesting they could be effective tools for patient education with physician guidance.

This study developed simulated skull models and a method to assess programming tool movements, selecting three models as most clinically relevant for hydrocephalus shunt valve programming.

This research highlights the potential of non-coding RNAs as biomarkers and therapeutic targets in dermatology, while experimental studies on a unique GVM case suggest CCM2L may modulate disease severity, advancing understanding of genetic mechanisms in rare skin disorders.

In this study, reflectance confocal microscopy was shown to provide early insights into sub-clinical treatment progress in patients with androgenetic alopecia, potentially offering advantages over global photography.

This study developed a high-resolution phototrichogram system that can automatically and accurately assess hair growth metrics in cosmetic trials, achieving over 90% correlation with manual measurements.

This study developed and tested a new algorithm for analyzing coat and skin coloration in laboratory animals, using digital images and hierarchical color clustering, which effectively quantified color proportions and tracked changes over time without specialized software.

This study introduces a novel image enhancement method that significantly improves the visual quality of low-light sports images by utilizing improved bilateral filtering and the CLAHE algorithm, achieving a 65.24% improvement in color and edge detail preservation compared to state-of-the-art methods on the LOL dataset.

In this study, Gaussian process regression models combined with Grey Wolf optimization were used to predict and optimize phenolic and flavonoid content extraction from Carthamus caeruleus L. rhizomes, showing high accuracy and helping improve understanding and extraction processes through a new interactive tool.