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The new method is rapid, sensitive, and accurate for evaluating the quality of the medicinal herb Platycladi cacumen.

A new teledermatology system improved access and reduced wait times for skin care.

GLI1 RNA CISH effectively identifies basal cell carcinoma but is less specific for benign follicular tumors.

3D ultrasound can detect hair follicle changes and disease phases in alopecia areata.

TAF can be identified by excessive skin thickening and clogged hair follicles, helping distinguish it from similar skin conditions.

OCT can identify hair structures, but chemical treatments can damage them.

AI improves medical care by enhancing diagnosis and treatment for better patient outcomes.

The device accurately estimates natural hair color at the roots in real time.

AFM helps study hair surfaces for dermatology, cosmetics, and forensics.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

Multiphoton microscopy can effectively image live cells in cornea, skin, and hair follicles over time.

Machine learning can predict hair health accurately using personal data.

Scalp coverage scoring accurately measures hair loss and treatment effectiveness.

A new method uses expert reviews of home videos to objectively assess children's developmental milestones in single-arm trials.

A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.

The combined hair transplant technique was effective and had high patient satisfaction.

Melanin density affects hair color, and this method can help in cosmetic assessments and diagnosing hair diseases.

Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.

Alopecic hair has more irregular structures and chemical changes than normal hair, reducing its strength.

Researchers found a white halo around hair in most patients with a specific type of hair loss, which helps in early diagnosis and treatment.

The model accurately identifies hair diseases using deep learning.

The system can automatically identify different hair and scalp conditions using machine learning.

In vivo reflectance confocal microscopy is useful for evaluating hair shaft diseases but needs improvement for deeper hair follicle issues.

The framework helps develop medical apps on mobile devices to reduce reliance on desktop computers.

An algorithm was created to simplify lip reconstruction after surgery.

Live imaging helps us understand skin immune responses and develop treatments.

The MIRA technique improves cellulite treatment results and patient satisfaction.

The prototype for analyzing skin aging works technically and clinically.

Hair mass index is a precise way to measure hair loss in chemotherapy patients.

A new system for classifying curly hair types using precise measurements can improve hair care products and cultural inclusion.