research 203 Automated skin surface phenotype for melanoma risk assessment
An automated method accurately assesses melanoma risk using 3D body images to analyze skin traits.
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540-570 / 1000+ results research Rethinking pattern hair loss classification in the era of trichoscopy and artificial intelligence
A hybrid model using traditional methods, trichoscopy, and AI improves hair loss assessment.
research Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study
ChatGPT is preferred for creating dermatology patient handouts, but all models can be useful with oversight.
research Patterns of coverage. Uniform versus graded density.
The uniform density approach in hair restoration is less noticeable in situations like wind or exercise.
research 200 Deep learning model to predict sentinel lymph node status in melanoma patients
The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.
research Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research 944 Non-coding double stranded RNA induces retinoic acid synthesis and retinoid signaling to control regeneration
Non-coding RNA boosts retinoic acid production and signaling, aiding regeneration.
research Generative adversarial networks for anonymous acneic face dataset generation
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
research A Comparative Study of Machine Learning Algorithms in Predicting Mental Disorders
Machine learning can accurately predict mental disorders.
research Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
research Generative Artificial Intelligence Tools: Evaluating Ways to Automate Your SALT (GATEWAYS) Scoring of Alopecia Areata
Generative AI tools like GPT-4o can effectively automate SALT scoring for alopecia areata, matching clinician accuracy.
research Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
Deep learning effectively diagnoses scalp disorders, but improvements are needed.
research Hair cluster detection model based on dermoscopic images
The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
research A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women
The model accurately predicts skin conditions in Korean women using genetic information, aiding personalized skincare.
research A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis
A new method measures mouse hair loss using shades of gray.
research The innervation of the mystacial pad in the adult rat studied by anterograde transport of HRP conjugates
The mystacial pad's innervation in adult rats is more complex than previously thought.
research Aspect-Level Drug Reviews Sentiment Analysis Based on Double BiGRU and Knowledge Transfer
A model called PM-DBiGRU was developed for analyzing sentiments in drug reviews, and it performed better than other models, but struggled with complex sentences and situations requiring background knowledge.
research High migratory activity of dermal sheath cup cells associated with the clinical efficacy of autologous cell-based therapy for pattern hair loss
Cells that move well may improve hair loss treatments by entering hair follicles.
research 706 Predictive modeling of patient response to JAK/STAT inhibitors and dynamic patient-matching
Machine learning can predict how well patients with alopecia areata will respond to certain treatments.
research Lentiviral Arrays for Live-cell Dynamic Monitoring of Gene and Pathway Activity During Stem Cell Differentiation
The lentiviral array can monitor and predict gene activity during stem cell differentiation.
research Incidence of alopecia in brain tumour patients treated with pencil scanning proton therapy and validation of existing NTCP models
A standardized scoring system is needed to improve model reliability for predicting hair loss in brain tumor patients treated with proton therapy.
research Applying Machine Learning Technique on Registry Data to Assess Factors Associated With In-Hospital Mortality Among STEMI Patients With Hypertension
research LGR5 is a Marker of Poor Prognosis in Glioblastoma and is Required for Survival of Brain Cancer Stem‐Like Cells
High LGR5 levels in glioblastoma indicate poor prognosis and are essential for cancer stem cell survival.
research A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.
research Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
Machine learning and single-cell analysis improve understanding and treatment of wound healing.
research A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis
Researchers developed a new, precise method to measure hair loss in mice using image analysis.
research Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology
The new AI system improves remote skin condition diagnosis and access to care.
research Automating Hair Loss Labels for Universally Scoring Alopecia From Images
The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.
research Compartmentalized Epidermal Activation of β-Catenin Differentially Affects Lineage Reprogramming and Underlies Tumor Heterogeneity
Activating β-catenin in different skin stem cells causes various types of hair growth and skin tumors.
research Testing the impact of trait prevalence priors in Bayesian-based genetic prediction modeling of human appearance traits
Using trait prevalence priors in genetic prediction models for appearance traits is currently impractical due to limited knowledge and potential accuracy issues.