research The laboratory in the multidisciplinary diagnosis of differences or disorders of sex development (DSD)
Diagnosing sex development disorders requires combining medical history, physical exams, imaging, lab tests, and genetic data.
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research Androgenetic alopecia. Diagnostic and therapeutic recommendations of the Polish Dermatological Society
Use trichoscopy to diagnose hair loss; treat with minoxidil, finasteride, or dutasteride; consider platelet-rich plasma and spironolactone.
research A Machine Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
The system can automatically identify different hair and scalp conditions using machine learning.
research DEEP REVIEW ON ALOPECIA AREATA DIAGNOSIS FOR HAIR LOSS-RELATED AUTOIMMUNE DISORDER PROBLEM
Machine learning and deep learning can effectively diagnose alopecia areata.
research Dermatoses of Pregnancy - Clues to Diagnosis, Fetal Risk and Therapy
The document concludes that specific itchy skin diseases during pregnancy have varying fetal risks and treatments, including corticosteroids and other medications.
research Trichotillomania: a case report with clinical and dermatoscopic differential diagnosis with alopecia areata
Trichotillomania is often misdiagnosed as alopecia areata and can be treated effectively with N-acetylcysteine and psychotropic drugs.
research Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms
Researchers found four genes that could help diagnose severe alopecia areata early.
research Methods of Using of Cosmeceuticals in Trichological Practice
Use personalized cosmeceuticals for safe, effective hair and scalp treatment.
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 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 Automating Hair Loss Labels for Universally Scoring Alopecia From Images
The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.
research Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
research Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria
The research found that PCOS has common genetic factors regardless of how it is diagnosed and is linked to metabolic and reproductive issues.
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 Dermoscopy: distinguishing malignant tumors from benign
Dermoscopy greatly improves melanoma diagnosis and reduces unneeded surgeries.
research CZY SZTUCZNA INTELIGENCJA MA I BĘDZIE MIAŁA WPŁYW NA EFEKTYWNIEJSZE LECZENIE W CHIRURGII NACZYNIOWEJ?
AI improves vascular surgery by enhancing diagnostics, planning, and monitoring.
research SYNERGIA AI I MEDYCYNY: SZANSE I WYZWANIA
AI is transforming healthcare by improving diagnostics and therapy, despite challenges with data and trust.
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 Dermoscopy: what's new?
Dermoscopy has improved skin cancer diagnosis and has expanding applications in dermatology, but requires staying updated with new research and techniques.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research MULTIOMICS – A NEW EXCITING ERA OF RESEARCH
Multiomics is revolutionizing biology by enabling breakthroughs in research and disease diagnosis.
research The use of auto-antibody testing in the evaluation of interstitial lung disease (ILD) – A practical approach for the pulmonologist
Auto-antibody testing is a useful but not definitive tool in diagnosing interstitial lung diseases, and using a specific algorithm could make testing more cost-effective.
research Abstracts
The document reports findings on genetic research, including ethical concerns about genome editing, improved diagnosis of mitochondrial mutations, solving inherited eye diseases, confirming gene roles in epilepsy, linking a gene to aneurysms, and identifying genes associated with age-related macular degeneration.
research Disorders of the Hair and Nails
The document reviews various hair and nail disorders, their causes, and treatments, emphasizing the need for proper diagnosis and the link between nail changes and systemic diseases.
research Hair loss.
Up to 50% of scalp hair can be lost before it appears thin, and treatment is only needed for hair loss caused by diseases or deficiencies.
research Androgen- and Estrogen-Secreting Adrenal Cancers
Rare adrenal cancers that secrete androgens or estrogens have a poor prognosis and are treated primarily with surgery.
research SZTUCZNA INTELIGENCJA (AI). CZY TO JUŻ ŚWIT DLA SMART BEAUTY I SMART DERMATOLOGY?
AI is advancing in dermatology and cosmetology, raising questions about its potential and trustworthiness.
research NOWA ERA W KARDIOCHIRURGII – CHIRURGIA ROBOTOWA I SZTUCZNE SERCE
Robotic surgery and artificial hearts are revolutionizing cardiac surgery.
research Presence and future of dermoscopy
Dermoscopy is becoming essential for diagnosing skin conditions and is expected to be a standard tool for dermatologists.
research THE POTENTIAL OF AI TO ENHANCE MEDICAL EDUCATION: OPPORTUNITIES, CHALLENGES, AND ETHICAL CONSIDERATIONS
AI can greatly improve medical education by personalizing learning and enhancing skills, but challenges like cost, training, and ethics need addressing.