AIMC Topic: Hair

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Application of machine learning for identification of key exposure predictors for heavy metal accumulation in hair of traffic police officers in Tehran.

The Science of the total environment
In order to determine variability and measure the major exposure factors affecting the levels of hazardous metals (such as Fe, Mn, Ni, Pb, As, Cr, and Cu) in the scalp hair of Tehran traffic police personnel, an advanced statistical method is used. T...

DyeSPY: Establishing the First Forensic SERS Reference for Hair Dye Colorant Evidence.

Analytical chemistry
Hair dyeing is a widespread practice with potential forensic value in individual identification, yet most analytical approaches are destructive, time-intensive, or lack sensitivity for trace residues. Surface-enhanced Raman spectroscopy (SERS) offers...

Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.

Scientific reports
Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To addr...

Identification of different hair dyes in dyed hair using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) and Surface Enhancing Raman Spectroscopy (SERS).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Forensic analysis of dyed hair by using Attenuated Total Reflectance Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Surface Enhanced Raman Spectroscopy (SERS) was undertaken to differentiate between commercial hair dye brands. Human hair sam...

Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal.

Journal of biomedical optics
SIGNIFICANCE: Early detection of melanoma is crucial for improving patient outcomes, and dermoscopy is a critical tool for this purpose. However, hair presence in dermoscopic images can obscure important features, complicating the diagnostic process....

Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.

The Journal of investigative dermatology
Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learnin...

Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.

Scientific reports
Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health...

An all-optical multidirectional mechano-sensor inspired by biologically mechano-sensitive hair sensilla.

Nature communications
Mechano-sensitive hair-like sensilla (MSHS) have an ingenious and compact three-dimensional structure and have evolved widely in living organisms to perceive multidirectional mechanical signals. Nearly all MSHS are iontronic or electronic, including ...

Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Int...

Integrated convolutional neural network for skin cancer classification with hair and noise restoration.

Turkish journal of medical sciences
BACKGROUND/AIM: Skin lesions are commonly diagnosed and classified using dermoscopic images. There are many artifacts visible in dermoscopic images, including hair strands, noise, bubbles, blood vessels, poor illumination, and moles. These artifacts ...