AIMC Topic: Hair

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A challenge of deep-learning-based object detection for hair follicle dataset.

Journal of cosmetic dermatology
BACKGROUND: Deep-learning object detection has been applied in various industries, including healthcare, to address hair loss.

Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia.

Acta dermato-venereologica
Since the results of basic and specific classification in male androgenetic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a ...

Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs.

Scientific reports
Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional i...

Light-Fueled Polymer Film Capable of Directional Crawling, Friction-Controlled Climbing, and Self-Sustained Motion on a Human Hair.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recent efforts in stimuli-responsive soft materials have enabled wirelessly controlled actuation with increasing degrees of freedom, yielding miniature robots capable of various locomotion in open environments such as on a plane or inside fluids. How...

Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach.

Computational and mathematical methods in medicine
Alopecia areata is defined as an autoimmune disorder that results in hair loss. The latest worldwide statistics have exhibited that alopecia areata has a prevalence of 1 in 1000 and has an incidence of 2%. Machine learning techniques have demonstrate...

Artificial Intelligence in hair research: A proof-of-concept study on evaluating hair assembly features.

International journal of cosmetic science
OBJECTIVE: The first objective of this study was to apply computer vision and machine learning techniques to quantify the effects of haircare treatments on hair assembly and to identify correctly whether unknown tresses were treated or not. The secon...

Biomimetic Hairy Whiskers for Robotic Skin Tactility.

Advanced materials (Deerfield Beach, Fla.)
Touch sensing is among the most important sensing capabilities of a human, and the same is true for smart robotics. Current research on tactile sensors is mainly concentrated on electronic skin (e-skin), but e-skin is prone to be easily dirtied, dama...

Combining Deep Learning With Optical Coherence Tomography Imaging to Determine Scalp Hair and Follicle Counts.

Lasers in surgery and medicine
BACKGROUND AND OBJECTIVES: One of the challenges in developing effective hair loss therapies is the lack of reliable methods to monitor treatment response or alopecia progression. In this study, we propose the use of optical coherence tomography (OCT...

Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network.

Biointerphases
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and e...