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- Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
- TTNPB Promotes Human Pluripotent Stem Cell‐to‐Neural Stem Cell Transition via Modulation of Chromatin Accessibility and the S‐(5′‐adenosyl)‐L‐homocysteine/Choline Metabolic Network
- Hair Loss Diagnosis Using Artificial Neural Networks
- Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study
- Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
- Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images
- Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
- A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
- 42063 Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images
- The Dark Side of the Language: Syntax-Based Neural Networks Rivaling Transformers in Definitely Unseen Sentences
- Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
- Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
- Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
- DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network
- In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
- Baseline Neutrophil-to-Lymphocyte Ratio Stratifies Early Trichoscopic Response to Platelet-Rich Plasma–Based Regimens in Non-Scarring Alopecia: A Real-World Cohort with Internal Validation Using an Interpretable Neural Network
- Generative adversarial networks for anonymous acneic face dataset generation
- Innervated Biomaterials for Improved Cutaneous Wound Healing: A Review of Recent Advancements and Future Prospects
- Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
- Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
- Hair cluster detection model based on dermoscopic images
- 1040 Photographing alopecia - How many pixels are needed for clinical evaluation?
- Progress and perspectives on hydrogel-assisted skin organoids
- Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
- “Bioprinting of Hair: How Far We Proceeded and What Needs to be Done”
- A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
- 203 Automated skin surface phenotype for melanoma risk assessment
- Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
- Prediction of Alopecia Areata using CNN