research DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.
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research A new practical classification for spatial distribution and morphology of human hair: Ahmad's LGMA classification
Dr. Muhammad Ahmad created a hair classification system to help improve hair restoration surgery outcomes.
research Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
Neurospectrum effectively analyzes neural signals to predict and identify brain activity patterns better than traditional methods.
research Enhancing Low-Light Sports Motion Images with Improved Bilateral Filtering and Auto MSRCR
The method greatly improves low-light sports images' quality and reduces artifacts.
research Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
Adjusting AI training data for skin condition distribution improves accuracy across different clinical settings.
research A comparative study of linear and nonlinear ANNs as classifiers of animal hair fibers
Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.
research GAN-Based ROI Image Translation Method for Predicting Image after Hair Transplant Surgery
The new method predicts post-hair transplant images more accurately than other methods.
research Big Self-Supervised Models Advance Medical Image Classification
Self-supervised learning improves medical image classification accuracy.
research Activating an adaptive immune response from a hydrogel scaffold imparts regenerative wound healing
Hydrogel scaffolds can help wounds heal better and grow hair.
research Prediction of gain-of-function and loss-of-function mutations using Combined Annotation Dependent Depletion (CADD)
C-scores can help predict gain-of-function and loss-of-function mutations.
research Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
The model accurately diagnoses hair diseases with 95% accuracy using deep learning.
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 A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
The system effectively detects scalp diseases and classifies hair fall stages with high precision.
research rwSALT: a regrowth-weighted SALT score providing direct pixel-level measurement rather than visual estimation
rwSALT provides precise hair regrowth measurement from scalp photos.
research A genetically informed brain atlas for enhancing brain imaging genomics
GIANT improves brain imaging by using genetics to better map brain regions.
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 Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity
Choosing the right model order in brain connectivity analysis can affect the detection of differences between healthy individuals and those with seasonal affective disorder.
research Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.
research A Segmental Approach to Scoring Hair Density from Global Images: Introducing the Scalp Coverage Scoring Method for Clinical Trials and Practice
The Scalp Coverage Scoring method reliably measures hair density from images.
research Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
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 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 DEEP REINFORCEMENT LEARNING FOR SCALABLE CONTROL OF BOOLEAN MODELS IN THE CONTEXT OF CELLULAR REPROGRAMMING
pbn-STAC effectively finds strategies for cellular reprogramming using deep reinforcement learning.
research Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study
AI can accurately diagnose and assess alopecia areata using scalp images.
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 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 NLKFill: high-resolution image inpainting with a novel large kernel attention
NLKFill improves high-resolution image inpainting by effectively capturing image details and enhancing speed.
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 203 Automated skin surface phenotype for melanoma risk assessment
An automated method accurately assesses melanoma risk using 3D body images to analyze skin traits.