research Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration
AI in hair and scalp analysis shows promise but lacks real-world clinical integration and validation.
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research Computerized morphometry and three-dimensional image reconstruction in the evaluation of scalp biopsy from patients with non-cicatricial alopecias
The study suggests computer-assisted analysis of scalp biopsies could improve hair loss diagnosis but needs more validation.
research Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
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 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 A Segmental Approach to Scoring Hair Density from Global Images: Introducing the Scalp Coverage Scoring Method for Clinical Trials and Practice
The Scalp Coverage Scoring method reliably measures hair density from images.
research “TRICHOASSIST” Trichogram Hair and Scalp Feature Extraction and Analysis Using Image Processing
"TRICHOASSIST" is a system that analyzes hair and scalp images to help diagnose scalp diseases.
research Scalp lesions, in images
Different scalp conditions can lead to hair loss or tumors, with treatments varying from creams to surgery; early detection is crucial.
research Quantification of hair follicle parameters using computer image analysis: A comparison of androgenetic alopecia with normal scalp biopsies
Hair follicles are smaller in people with androgenetic alopecia compared to those with normal scalps.
research Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest treatments.
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 Dirty Dots as a Normal Trichoscopic Finding in the Elderly Scalp
Dirty dots are a common scalp finding in elderly women and can be washed away with shampoo.
research ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.
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 Supplementary Images & Discussion - Tolerated, Effective, Successful: Low Dose Oral Minoxidil for Treating Alopecia, A 3-Year North American Retrospective Case Series
Low-dose oral minoxidil effectively treats hair loss with good tolerance.
research Standardization and Validation of Scalp Surface Area Measurement and Assessment of Interchangeability Between Two Independent Methodologies for Head Crown Area and Total Hair Count Measurement
Two methods reliably measure scalp area and hair count.
research Hair and scalp disease detection using deep learning
Deep learning can effectively detect hair and scalp diseases early.
research Investigating the relationship between self-esteem and body image concern and self-compassion in women with female pattern hair loss
Women with female pattern hair loss tend to have lower self-esteem and body image concerns, but higher self-esteem is linked to more self-compassion.
research ISID0192 - Three-dimensional ultra-high frequency ultrasound facilitates image processing to visualize microstructural changes of hair follicles and detects distinct disease phases of alopecia areata
3D-ultrasound can non-invasively detect and predict alopecia areata phases and outcomes.
research Needle Transportable Semi-Automatic Hair Follicle Implanter and Image-Based Hair Density Estimation for Advanced Hair Transplantation Surgery
A new semi-automatic hair implanter could make hair transplants easier, more successful, and more accessible.
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 Abstract presented at the Joint meeting of the 21st meeting of the Japanese Research Society of clinical anatomy and the 6th meeting of the Society for aging image
High-resolution imaging is crucial for diagnosing and planning treatments in clinical anatomy and aging.
research Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
research Densitometry and Video-microscopy
Densitometry and video-microscopy are precise for evaluating hair loss and transplant success but need special equipment and training.
research 701 Rethinking alopecia scores: Correlating measures of hair loss with established photographic scoring systems
The conclusion suggests that a new system for measuring hair loss could be created using automated analysis of photographs.
research Photographic Imaging in Hair Loss
Photographic imaging is crucial for documenting and managing hair loss, requiring careful preparation and standardization to be effective.
research Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
research SnapshotDx Quiz: September 2016
Scalp psoriasis features reversible hair loss and specific immune activation, with no significant hair follicle damage.
research เครองมอประมวลผลภาพดจทลสำหรบวดขอมลของเสนผมบนหนงศรษะของผปวยโรคผมบางทางพนธกรรม
The tool accurately measures hair count and size on scalps with 79.45% and 68.19% accuracy, respectively.
research Identification of Stress-Related Hair Loss and Prevention Through the Application of a KNN Model Based on Machine Learning
Machine learning can predict stress-related hair loss and suggest prevention tips.