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.
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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 301 Whole exome sequencing in AA patients identifies a hotspot mutation in the type II hair keratin gene, KRT82
A mutation in the KRT82 gene is significantly associated with Alopecia Areata.
research Genome-wide detection and sequence conservation analysis of long non-coding RNA during hair follicle cycle of yak
The study found key long non-coding RNAs involved in yak hair growth cycles.
research Using artificial intelligence to compute Severity of Alopecia Tool scores
AI can accurately measure hair loss severity in alopecia areata.
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 DKK4 as a novel candidate marker for Zhexi Angora rabbit wool quality
DKK4 can be used to improve wool quality in Zhexi Angora rabbits.
research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
VGG16 and VGG19 are the most accurate for classifying scalp and hair diseases.
research 498 A Multi-Omics Approach to create a Human Hair Atlas for healthy and AGA models
The research aims to better understand hair follicle regulation and find new treatments for hair loss.
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 Hair Loss Diagnosis Using Artificial Neural Networks
Neural networks can effectively predict hair loss.
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 A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment
The machine learning model effectively assesses the severity of hair loss and could help dermatologists with treatment decisions.
research Rule-out test for autism using machine-learning analysis of molecular temporal dynamics in hair - a multicenter study
A machine-learning test using hair can help detect autism early in infants.
research Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases
The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.
research Localized in vivo genotypic and phenotypic correction of the albino mutation in skin by RNA-DNA oligonucleotide
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 An Integrated Approach for Diabetes Detection Using Fisher Score Feature Selection and Capsule Network
The method accurately detects diabetes with 94% effectiveness, reducing misdiagnosis risk.
research The Follicle Inference Engine (FIE): A stochastic digital twin and optimal experimental design framework for androgenetic alopecia
research Fine mapping of the human AR/EDA2R locus in androgenetic alopecia
Genetic marker rs12558842 strongly linked to male hair loss.
research Evaluation of Five Hundred Patients using Ahmad’s NPRT System for Documenting Male Pattern Baldness
Ahmad's NPRT system accurately documents and predicts male pattern baldness.
research Analysis of histology and long noncoding RNAs involved in the rabbit hair follicle density using RNA sequencing
Higher hair follicle density leads to more wool in rabbits, influenced by specific genes and lncRNAs.
research Comparison of Linear Regression, Decision Tree Regression, and Random Forest Regression Algorithms in Predicting Baldness Risk
Random Forest Regression is best for predicting baldness risk.
research Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep
Key genes IRF2BP2 and EGFR are linked to Hetian sheep's double-coat fleece.
research Differential expression patterns of specific long noncoding RNAs and competing endogenous RNA network in alopecia areata
Specific RNA patterns are linked to alopecia areata.
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 Integrated analysis of lncRNAs and mRNAs by RNA-Seq in secondary hair follicle development and cycling (anagen, catagen and telogen) of Jiangnan cashmere goat (Capra hircus)
lncRNAs play a key role in hair follicle development, affecting cashmere quality and yield.
research Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies hair diseases.
research Fractal geometry and topology abstracted from hair fibers
Hair fibers have fractal patterns with properties related to the golden mean, which may affect their functionality.
research An Integrated Analysis of Cashmere Fineness lncRNAs in Cashmere Goats
lncRNA XLOC_008679 and gene KRT35 affect cashmere fineness in goats.