research Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.
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research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
research Bayesian measurement-error-driven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia
Model improves accuracy in predicting hair loss effects.
research Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
research Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
The model can effectively help diagnose meibomian gland dysfunction automatically.
research Role of Robotics in Neuromodulator and Filler Injections of Face
Robotics in facial injections offers precision but is costly and lacks personal touch.
research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research The role of AUTS2 in neurodevelopment and human evolution
The AUTS2 gene is linked to neurological disorders and may affect human brain development and cognition.
research Ewha Medical Journal’s inclusion in PubMed Central and PubMed, and artificial intelligence and guidelines in this issue
The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.
research Where Truth Lies in Advertising
Advertising mixes truth and imagination to persuade consumers that products are essential.
research Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet v2 for Multi-Class Classification
The model accurately identifies hair diseases using deep learning.
research Automated quantitative analysis of coat and skin coloration in laboratory animals
A new algorithm accurately analyzes animal coat and skin colors quickly and easily.
research Modeling the dynamics of human hair cycles by a follicular automaton
The model accurately simulates human hair growth and hair loss patterns.
research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
research An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
research Hair Tone Estimation at Roots via Imaging Device with Embedded Deep Learning
The device accurately estimates natural hair color at the roots in real time.
research Artificial skin for wound healing
Artificial skin is improving wound healing and shows potential for treating different types of wounds.
research NIR‐Responsive Multifunctional Artificial Skin for Regenerative Wound Healing
The artificial skin promotes better wound healing and skin regeneration.
research Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research Use of Robotic Systems in Aesthetic/Cosmetic Plastic Surgery—A Review Article
Robotic systems in cosmetic surgery are promising but need more development and research.
research The pathogenesis model research of alopecia areata: from animal experiments to mathematical prediction models
Animal and mathematical models help understand and develop treatments for alopecia areata.
research LB1040 Machine learning-based predictive model with routine blood work identifies moderate-severe alopecia areata
Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.
research The Follicular Automaton Model: Effect of Stochasticity and of Synchronization of Hair Cycles
The model showed that randomness accurately describes individual hair growth cycles and that synchronization can cause large fluctuations not seen in humans.
research Letters to the Editors: Re: Role of automated devices in hair restoration
Automated devices are beneficial for hair restoration.
research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.
research Artificial Hair: By the Dawn to Automatic Biofibre® Hair Implant
Biofibre® hair implants are safe and effective for alopecia when proper procedures are followed, with high patient satisfaction.
research The mind-body problem; three equations and one solution represented by immaterial-material data
The mind and body don't directly interact; the mind acts as an interface linking abstract and physical data.
research Organoid‐Guided Precision Medicine: From Bench to Bedside
Organoid technology is improving personalized medicine by better predicting drug responses and treatments.
research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.