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.
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research The utility of the classification tools in National Institute of Health and Clinical Excellence’s Clinical Knowledge Summaries for primary care
Nearly half of the classification tools in the National Institute of Health and Clinical Excellence's Clinical Knowledge Summaries might not effectively guide management for general practitioners.
research Geometric classification of scalp hair for valid drug testing, 6 more reliable than 8 hair curl groups
A 6-group hair classification is more reliable for drug testing than an 8-group system.
research A new practical classification for spatial distribution and morphology of human hair: Ahmad's LGMA classification
Dr. Muhammad Ahmad created a hair classification system to help improve hair restoration surgery outcomes.
research A Combinatorial Tweet Clustering Methodology Utilizing Inter and Intra Cosine Similarity
The method effectively grouped tweets into categories without knowing the number of groups beforehand.
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 574 A deep learning classifier for the analysis of tiger tail banding in trichothiodystrophy
A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.
research A discriminant multi-scale histopathology descriptor using dictionary learning
The new image descriptor helps identify skin cancer structures with good accuracy.
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 203 Automated skin surface phenotype for melanoma risk assessment
An automated method accurately assesses melanoma risk using 3D body images to analyze skin traits.
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.
research Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research Reliability of the pattern hair loss classifications: A comparison of the basic and specific and N orwood–H amilton classifications
The BASP classification is more reliable than the Norwood-Hamilton for classifying hair loss in men and women.
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 Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study
Using deep learning to predict gene expression from images could help assess colorectal cancer metastasis.
research Modified basic and specific (BASP) classification for pattern hair loss
The modified hair loss classification is more detailed but less user-friendly.
research Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.
research Shape Variability and Classification of Human Hair: A Worldwide Approach
Human hair can be classified into eight types based on physical features, not ethnicity.
research HOW AI CAN SUPPORT FOLLICULAR LYMPHOMA DIAGNOSIS
AI can help diagnose Follicular Lymphoma by accurately identifying specific cell types.
research Clinical diagnosis of COVID-19: a prompt, feasible, and sensitive diagnostic tool for COVID-19 based on a 1,757-patient cohort (The AndroCoV Clinical Scoring for COVID-19 diagnosis)
The study concludes that the new clinical scoring system is a quick, low-cost, and accurate method for diagnosing COVID-19.
research How I Do It: Ahmad’s NPRT System: A New Classification System for Documenting Male Pattern Baldness
Dr. Muhammad Ahmad created a simpler system to better describe male pattern hair loss.
research Worldwide diversity of hair curliness: a new method of assessment
A new method accurately classifies hair types, showing global hair diversity.
research 200 Deep learning model to predict sentinel lymph node status in melanoma patients
The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.
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 Faculty Opinions recommendation of A new classification of pattern hair loss that is universal for men and women: basic and specific (BASP) classification.
The BASP classification is a detailed system for categorizing hair loss in both men and women, but it may be complex for beginners and not fully suitable for grading female hair loss.
research Linear Basal Cell Carcinomas
Dr. Connelly agrees that linear basal cell carcinomas might be more aggressive but highlights the study's lack of clear criteria to identify them.
research Boundary-aware Multi-stage with Mobile U-Net for Hair Segmentation in Dermoscopic Images
The new method improves hair segmentation in skin images, helping detect skin cancer more accurately.
research Classifications of patterned hair loss: a review
Different types of hair loss need specific treatments, and while many classification systems exist, each has its flaws; more research is needed to refine these systems and treatments.
research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
research A machine learning and network framework to discover new indications for small molecules
A machine learning model called CATNIP can predict new uses for existing drugs, like using antidepressants for Parkinson's disease and a thyroid cancer drug for diabetes.