research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
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90-120 / 1000+ resultsresearch AI04 A novel machine learning software application for prognosticating cutaneous malignant melanoma based on data from 78 351 patients from the Surveillance, Epidemiology, and End Results (SEER) database
The new AI software predicts melanoma outcomes more accurately than traditional methods.
research Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
Deep learning and explainable AI are improving scalp disorder diagnosis, but challenges in transparency and data quality remain.
research Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images
Deep learning can improve non-invasive alopecia diagnosis using hair images.
research Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study
Using deep learning to predict gene expression from images could help assess colorectal cancer metastasis.
research Gene trajectory inference for single-cell data by optimal transport metrics
research Big Self-Supervised Models Advance Medical Image Classification
Self-supervised learning improves medical image classification accuracy.
research Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Fine-tuned BERT models are better than LLMs for detecting bias in medical data.
research The Dark Side of the Language: Syntax-Based Neural Networks Rivaling Transformers in Definitely Unseen Sentences
Syntax-based neural networks can match Transformers in handling unseen sentences.
research AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
SL-HyDE improves medical information retrieval accuracy without needing labeled data.
research MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov–Arnold network and data augmentation
MultiKano accurately identifies cell types in complex data better than existing methods.
research Detection of mutations in the CYP21A2 gene: genotype-phenotype correlation in Slovenian couples with conceiving problems
Genetic screening can help diagnose and manage infertility in Slovenian couples.
research Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
research Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification
The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.
research Understanding hair disorders through artificial intelligence: DALLE's role in image creation
DALL-E 2 can create realistic hair images but struggles with specific hair disorders.
research Hair and Scalp Disease Detection using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed images.
research In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
research Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation
The framework helps predict adverse effects of blood thinners, improving drug selection for atrial fibrillation.
research Boundary-aware Multi-stage with Mobile U-Net for Hair Segmentation in Dermoscopic Images
The new method improves hair segmentation in skin images, helping detect skin cancer more accurately.
research Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data
AI can improve early diagnosis and classification of PCOS, aiding in prevention of related health issues.
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 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 ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREDICTION OF COVID-19 MORTALITY AND ITS RELATED FACTORS: A MULTI-CENTER STUDY
AI can effectively predict COVID-19 mortality risk using patient data.
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 JAK-centric explainable few-shot gene-expression diagnosis framework for alopecia via MultiPLIER priors and relation-style set-to-set comparison
A new method helps diagnose alopecia areata using specific gene markers and could guide targeted treatments.
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 Viable terminal scalp hair follicles constitute a necessary and sufficient biological end‐organ that conditions clinical efficacy of finasteride in males with male pattern hair loss without implying reversal of miniaturized follicles
Finasteride works for hair loss by maintaining existing hair follicles, not reversing miniaturization.
research Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest treatments.
research Generative adversarial networks for anonymous acneic face dataset generation
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
research FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.