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.
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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 From Diagnosis to Prognosis: A Scoping Review of Artificial Intelligence in Alopecia Research
This scoping review systematically maps the use of AI and ML in alopecia research, highlighting the evolution from diagnostic tools to more advanced prognostic frameworks. It identifies the dominance of CNNs for image-based diagnosis, limited multimodal integration, and a lack of prognostic or causal modeling. Key gaps include the need for generative prognosis frameworks, fairness evaluations, privacy-aware systems, and treatment-response prediction. The review provides a taxonomy of AI approaches, visual aids, and recommendations for future research, emphasizing the development of equitable AI tools for personalized prognosis and treatment planning in alopecia care.
research A Feature-Preserving Hair Removal Algorithm for Dermoscopy Images
The algorithm effectively removes hair from skin images, improving melanoma diagnosis accuracy.
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 Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization Criminisi algorithm
The new hair removal algorithm for skin images works better for detecting and fixing hair, improving melanoma diagnosis.
research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
research Deep Learning Based Non-Invasive Framework for Nutritional Deficiency Detection Using Hair and Nail Images
A deep learning method can detect nutritional deficiencies from hair and nail images with 89% accuracy.
research Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest treatments.
research Enhanced stratification of male pattern hair loss using AI through novel loss region ratio analysis
AI improves accuracy and consistency in diagnosing male pattern hair loss.
research Hair and scalp disease detection using deep learning
Deep learning can effectively detect hair and scalp diseases early.
research Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology
The new AI system improves remote skin condition diagnosis and access to care.
research Usefulness of high-frequency ultrasonography in the assessment of alopecia areata – comparison of ultrasound images with trichoscopic images
High-frequency ultrasonography can be a useful tool for diagnosing different stages of alopecia areata, a type of hair loss.
research Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
The model can effectively help diagnose meibomian gland dysfunction automatically.
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 Flow-guided hair removal for automated skin lesion identification
The new method removes hair from skin images quickly and accurately to help identify skin lesions better.
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 Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.
research Establishment and validation of a hair fall atlas for Chinese men based on trichoscopy
A new tool helps better assess and treat hair loss in Chinese men.
research Carbobenzoxy-capped Phe-Lys(BODIPY TMR-X-acyloxymethyl ketone(QSY7)
Near-infrared probes can safely and effectively image cysteine protease activity for disease diagnosis.
research Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
The Advanced Precipitation U-Net Model improves early hair fall detection with 92% accuracy.
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 PÉ DIABÉTICO NA ATENÇÃO BÁSICA: UMA REVISÃO DE LITERATURA
The document concludes that Passiflora incarnata could help with anxiety, telemedicine might improve heart failure care, screen time for kids has increased, pregnant teens in Brazil are mostly okay with their body image, rare tuberculosis infection can occur after knee surgery, older and severely ill people are more likely to have long COVID-19 symptoms, HPLC might diagnose more diabetes cases, and psychiatrists should be involved in pain management.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research Primary Cicatricial Alopecias
Early diagnosis and aggressive treatment are key for managing rare scalp disorders that cause permanent hair loss.
research Dermoscopic characterization and clinical application of four facial erythematous inflammatory dermatoses
The document concludes that specific dermoscopic features can help diagnose different facial red skin conditions.
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.
research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research Critical study of hair growth analysis with computer-assisted methods
The Trichoscan system was found to be inaccurate for measuring hair growth, needing better software to be useful.