research ANÁLISES COMPUTACIONAIS PREDITIVAS COMO UM NOVO BIOPODER: MODIFICAÇÕES DO TEMPO NA SOCIEDADE DOS SENSORES
Predictive computational analyses have evolved biopower by using technology to track and predict individual and group behaviors.
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research DNA phenotyping: current application in forensic science
DNA phenotyping helps predict physical traits from DNA with varying accuracy and requires careful ethical and legal handling.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
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 Re-identification of anonymised MRI head images with publicly available software: investigation of the current risk to patient privacy
Standardized de-facing protocols can prevent identification from anonymized MRI images, enhancing privacy protection.
research Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study
AI can accurately diagnose and assess alopecia areata using scalp images.
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 706 Predictive modeling of patient response to JAK/STAT inhibitors and dynamic patient-matching
Machine learning can predict how well patients with alopecia areata will respond to certain treatments.
research Learning Curve for the Hair Implanter Pen
The Hair Implanter Pen increases speed and is gentle on grafts, with users mastering it after a few tries.
research Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
The new method improves skin cancer detection in imbalanced datasets.
research A Fuzzy Logic-Based Computational Framework for Precision Triage in Androgenetic Alopecia: A Simulated Biomarker-Driven Approach
The TPAP method effectively categorizes androgenetic alopecia patients with high accuracy, but needs real-world validation.
research PRECISE Scale: a quantitative classification for androgenetic alopecia and its application to hair transplantation
The PRECISE scale helps estimate how many grafts are needed for hair transplant based on the severity of hair loss.
research Penerapan Algoritma Naive Bayes untuk Prediksi Kerontokan Rambut
Naive Bayes algorithm can help predict hair loss risk early.
research Forensic DNA phenotyping: the need for proportionate regulation and judicial clarity in law enforcement
Unified regulations and ethical guidelines are needed for fair use of forensic DNA phenotyping.
research Collider bias undermines our understanding of COVID-19 disease risk and severity
Collider bias can distort our understanding of COVID-19 risk and severity.
research Transcriptional Profiling in Rat Hair Follicles following Simulated Blast Insult: A New Diagnostic Tool for Traumatic Brain Injury
Hair follicles can help diagnose traumatic brain injury quickly and non-invasively.
research Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing
Low-coverage sequencing is a cost-effective way to find genetic factors affecting rabbit wool traits.
research Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images
Deep learning can improve non-invasive alopecia diagnosis using hair images.
research Spatial transcriptomics of a giant pilomatricoma
The analysis of a large pilomatricoma revealed five distinct areas with different gene activity related to hair growth and tumor development.
research Cross-species amplification of human microsatellite markers in pig-tailed and stump-tailed macaques
Some human genetic markers work for genetic studies in pig-tailed and stump-tailed macaques, which can help in their conservation.
research Comparison of Linear Regression, Decision Tree Regression, and Random Forest Regression Algorithms in Predicting Baldness Risk
Random Forest Regression is best for predicting baldness risk.
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.
research Amplitude-guided deep reinforcement learning for semi-supervised layer segmentation
The new method improves accuracy in segmenting scalp tissue layers.
research Multi-ancestry tandem repeat association study of hair colour using exome-wide sequencing
Tandem repeats significantly influence hair color, especially darker shades, across different ancestries.
research Rethinking pattern hair loss classification in the era of trichoscopy and artificial intelligence
A hybrid model using traditional methods, trichoscopy, and AI improves hair loss assessment.
research Improving human forensics through advances in genetics, genomics and molecular biology
DNA profiling in forensics has improved, but predicting physical traits and ancestry from DNA has limitations and requires ethical consideration.
research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Design, proof-of-concept of single robotic hair transplant mechanisms for both harvest and implant of hair grafts
A single robotic system can accurately harvest and implant hair grafts, showing promise for real-world use.
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 Predictive Modeling of Hair Fall using Random Forest Algorithms
The model accurately predicts hair loss by analyzing various factors.