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- Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
- Automated measurement of hair density and projected apparent width via deep learning–based phototrichogram analysis
- Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
- Scalp disease analysis using deep learning models
- 574 A deep learning classifier for the analysis of tiger tail banding in trichothiodystrophy
- PEDL-XAI: A Hybrid Probabilistic Ensemble Deep Learning Approach for Hair Disorder Diagnosis
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
- Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
- Quantitative analysis and development of alopecia areata classification frameworks
- Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
- Analysis of modern approaches to hair care: An overview of products and techniques
- Analysis of modern approaches to hair care: An overview of products and techniques
- Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study
- Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
- Genetic and Environmental Factors Affecting Hair Density in East Asian Populations
- PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery
- Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging
- BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features
- A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
- Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
- The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline
- Vitadetect : Vitamin Deficiency Detection
- Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration
- Research Techniques Made Simple: Optical Clearing and Three-Dimensional Volumetric Imaging of Skin Biopsies
- Hair and scalp disease detection using deep learning
- Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
- ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
- Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers
- Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review