research CNN-KNN Model for Assessing Hair Health in Telogen Effluvium
The model accurately predicts hair breakage in Telogen Effluvium, aiding early detection and treatment.
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research Metabolic study of androsta-1,4,6-triene-3,17-dione in horses using liquid chromatography/high resolution mass spectrometry
The study found that urine metabolites M1b or M4 are the best indicators of ATD use in horses, with detection possible up to 77 hours in urine and 28 hours in blood.
research Alopecia classifications
Classifying alopecia helps diagnose and treat different types of hair loss accurately.
research Classification of hair disease
Classifying hair diseases, like alopecia, is difficult and needs more research to understand their causes.
research Human hair form. Morphology revealed by light and scanning electron microscopy and computer aided three-dimensional reconstruction.
Hair follicle shape determines hair type: curly, straight, or in-between.
research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition
The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
research Corkscrew Hair: A New Dermoscopic Sign for Diagnosis of Tinea Capitis in Black Children
Corkscrew hair may be a new sign for quickly diagnosing scalp fungus in black children.
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 Hair and scalp disease detection using deep learning
Deep learning can effectively detect hair and scalp diseases early.
research FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
research Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest treatments.
research Rethinking pattern hair loss classification in the era of trichoscopy and artificial intelligence
A hybrid model using traditional methods, trichoscopy, and AI improves hair loss assessment.
research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
research Hair cluster detection model based on dermoscopic images
The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
research DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.
research Hair casts: Classification, staining characteristics, and differential diagnosis
A new method helps identify and classify different types of hair casts.
research "Curly" Wood and Tiger Tails
Tiger tail bands in hair are caused by wavy hair fibers with melanin, unlike straight fibers in normal hair.
research Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research Validity of trichoscopy in the diagnosis of primary cicatricial alopecias
Trichoscopy is a useful method for identifying primary cicatricial alopecias and their specific types.
research The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline
The Hair Cell Analysis Toolbox automates and improves the analysis of cochlear hair cells using machine learning.
research Cicatricial alopecia: classification and histopathology
Early detection and histopathology are crucial to prevent permanent hair loss in cicatricial alopecia.
research 1375 Cutaneous adnexal cysts revisited: A novel classification based on morphological and molecular features
The conclusion introduces a new way to classify skin cysts using their shape and genetic markers.
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 ‘A detective look’ at hair biopsies from African-American patients
Recognizing specific features of African-American hair can help diagnose hair loss conditions.
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 A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones
ChatGPT is more accurate at diagnosing hair disorders in lighter skin tones than darker ones.
research Citrulline-containing basal cell carcinomas. Differentiation toward hair structures with induction of dermal hair papillae
Citrulline in certain skin tumors suggests they mimic hair growth, helping distinguish them from other cancer types.
research An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning
Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
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 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.