research Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text
The AI model DIET-AI effectively diagnoses skin diseases as well as doctors.
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research In Vivo Reflectance Confocal Microscopy in General Dermatology: How to Choose the Right Indication
Reflectance confocal microscopy is useful for diagnosing and monitoring skin diseases, but it has limitations and requires expertise for correct use.
research QuantAnts machines discover biomarkers for RAS signaling activation and design sgRNAs for CRISPR to target complex CD9, CD34, and CD74.
The QuantAnts machines can find cancer markers and create CRISPR targets for them.
research Correlation Between In Vivo Reflectance Confocal Microscopy and Horizontal Histopathology in Skin Cancer: A Review
Reflectance confocal microscopy reliably identifies skin cancer features like horizontal skin tissue sections.
research Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers
AI can accurately identify some cosmetic fillers in ultrasound images but needs improvement for others.
research A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors
Adding obesity data to machine learning models improves heart disease prediction accuracy.
research Estimating Scalp Moisture in a Hat Using Wearable Sensors
A hat with sensors can measure scalp moisture well, helping with hair care.
research Raman spectroscopy reveals biophysical markers in skin cancer surgical margins
Raman spectroscopy can help identify cancerous skin tissue during surgery.
research Amplitude-guided deep reinforcement learning for semi-supervised layer segmentation
The new method improves accuracy in segmenting scalp tissue layers.
research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.
research A novel hairless highly immunodeficient mice model optimized for in vivo imaging
The new Hairless R/J mice model improves imaging for tumor monitoring and cancer therapy evaluation.
research CHROMALENS PRECISION MAPPING AS AN INNOVATIVE SYSTEM FOR MICROZONAL HAIR ANALYSIS: STRUCTURE, ALGORITHM, AND COMPARISON WITH CLASSICAL COLORING TECHNIQUES
research Detecting shortcut learning for fair medical AI using shortcut testing
The ShorT method can detect and help reduce bias in medical AI by identifying shortcut learning.
research Prediction of cell states and key transcription factors of the human cornea through integrated single-cell omics analyses
A new tool accurately identifies human cornea cell states and key factors.
research A Comparative Study of Machine Learning Algorithms in Predicting Mental Disorders
Machine learning can accurately predict mental disorders.
research Discrimination between basal cell carcinoma and hair follicles in skin tissue sections by Raman micro-spectroscopy
Raman micro-spectroscopy can help distinguish basal cell carcinoma from hair follicles in skin tissue.
research The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline
The Hair Cell Analysis Toolbox automates and improves the analysis of cochlear hair cells using machine learning.
research AI04 A novel machine learning software application for prognosticating cutaneous malignant melanoma based on data from 78 351 patients from the Surveillance, Epidemiology, and End Results (SEER) database
The new AI software predicts melanoma outcomes more accurately than traditional methods.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
A new model can predict drug-disease links well, helping drug research.
research Reflectance confocal microscopy for scarring and non-scarring alopecia real-time assessment
Reflectance confocal microscopy can help tell apart scarring from non-scarring hair loss.
research 198 Reliable and interpretable segmentation for remote assessment of atopic dermatitis severity using digital images
The improved EczemaNet more reliably and clearly identifies and assesses the severity of atopic dermatitis from photos.
research Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research Raman investigation of in vivo radiation exposure on melanin in murine hair
Raman spectroscopy can detect radiation exposure in mouse hair with high accuracy for up to 7 days.
research Automating Hair Loss Labels for Universally Scoring Alopecia From Images
The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.
research Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification
The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.
research Reflectance confocal microscopy can differentiate dermoscopic white dots of the scalp between sweat gland ducts or follicular infundibulum
Reflectance confocal microscopy can tell apart white dots on the scalp as either sweat gland ducts or hair follicle openings.
research Deep Learning-based Automatic System for Diagnosis and Classification of Skin Dermatoses
The document concludes that an automatic system using deep learning can help diagnose skin disorders, but challenges and opportunities in this area remain.
research Multiphoton microscopic imaging of rabbit dorsal skin
Multiphoton microscopy effectively images rabbit skin structures in detail without staining and shows differences from human skin.
research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.