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.
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research BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features
BaldGraphFormer accurately predicts early baldness, aiding better hair loss treatment.
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 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 Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research HairLossMultinet: A Multi Scale Feature Fusion Method Using Deep Learning Approach
HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.
research Skin iconography analysis of traumatic anserine folliculosis: Skin image feature of TAF
TAF can be identified by excessive skin thickening and clogged hair follicles, helping distinguish it from similar skin conditions.
research SnapshotDx Quiz: September 2016
Scalp psoriasis features reversible hair loss and specific immune activation, with no significant hair follicle damage.
research Automated Trichoscopic Analysis for Hair Disorder Classification Using Semantic Segmentation
An automated system can accurately classify hair disorders using image analysis.
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 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 画像解析による抗がん剤脱毛頭皮の形態構造の検証
Scalp texture changes during hair regrowth after chemotherapy.
research Use of smartphones in telemedicine: Comparative study between standard and teledermatological evaluation of high-complex care hospital inpatients
Low-cost videomicroscopes might not be reliable for examining scalp disorders compared to standard methods.
research Use of low cost videomicroscopy vs standard videodermatoscopy in trichoscopy: A controlled, blinded noninferiority trial
Low-cost videomicroscopy may not be reliable for diagnosing hair loss compared to standard videodermatoscopy.
research Trichoscopic features of androgenetic alopecia according to the basic and specific (BASP) classification: A retrospective observational study
Trichoscopy helps detect and monitor androgenetic alopecia by identifying key scalp signs.
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 799 A Single Center Retrospective Cohort Study of Burn Scar Alopecia Surgery
Surgery for burn scar alopecia often involves multiple procedures, and treatment options should be more accessible to improve self-image.
research Feature characterization of scarring and non-scarring types of alopecia by multiphoton microscopy (Conference Presentation)
Multiphoton microscopy can effectively distinguish between scarring and non-scarring alopecia.
research Use of Low-Cost Videomicroscopy versus Standard Videodermatoscopy in Trichoscopy: A Controlled, Blinded Noninferiority Trial
Low-cost videomicroscopes are not as reliable as standard videodermatoscopes for scalp examination due to lower image quality.
research Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
AI can predict hair loss by analyzing genetic, scalp, and lifestyle data.
research Absence of vellus hair in the hairline: a videodermatoscopic feature of frontal fibrosing alopecia
Lack of small, fine hair on the front hairline is a key sign of frontal fibrosing alopecia.
research Mirror-image alopecia areata in mirror-image twins
Mirror-image twins can have alopecia areata on opposite sides of their heads.
research Comparative Analysis of Trichoscopic Features in Women with Androgenetic Alopecia and Telogen Effluvium
FAGA shows varied hair thickness, while TE has more empty follicles.
research AN OBSERVATIONAL STUDY TO STANDARDIZE THE DERMOSCOPIC FINDINGS OF NORMAL SCALP AND HAIRS IN MALE
Standardized data is essential for diagnosing scalp and hair conditions in males.
research Videodermoscopy in the evaluation of hair and scalp disorders
Videodermoscopy improves diagnosis of hair and scalp disorders and may reduce scalp biopsies.
research Dermatoscopic Findings in Androgenetic Alopecia
Trichoscopy helps diagnose and track treatment for hair loss in men and women.
research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
research The ProScope HR: A promising diagnostic tool
The ProScope HR is an effective, user-friendly, and affordable tool for diagnosing hair loss.
research Male Androgenetic Alopecia — Endotext
Male androgenetic alopecia (MAA) is a common, hereditary hair loss condition in men, linked to heart disease, and can be treated with minoxidil, finasteride, or hair transplantation.
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.