9 citations,
September 2022 in “Frontiers in Physics” The technique accurately identifies and evaluates hair follicle structures in skin.
April 2023 in “Journal of Investigative Dermatology” The improved EczemaNet more reliably and clearly identifies and assesses the severity of atopic dermatitis from photos.
The new algorithm removes hair from skin images better than previous methods, helping diagnose melanoma.
January 2018 in “Communications in computer and information science” Researchers developed a computer system to automatically diagnose hair loss by analyzing scalp images.
20 citations,
December 2017 in “Journal of Investigative Dermatology Symposium Proceedings” Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.
10 citations,
September 2020 in “Computational and Mathematical Methods in Medicine” Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
1 citations,
January 2023 in “IEEE access” Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
February 2024 in “Frontiers in physics” The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
139 citations,
December 2020 in “Cell Stem Cell” Male hormones affect COVID-19 severity and certain drugs targeting these hormones could help reduce the risk.
7 citations,
January 2023 in “Frontiers in cell and developmental biology” Celsr1 is crucial for skin cell alignment, while Celsr2 has little effect on this process.
5 citations,
April 2024 in “bioRxiv (Cold Spring Harbor Laboratory)” Aging skin shows thinner layers, fewer hair follicles, and new biomarkers like increased space between cells and smaller sebaceous glands.
5 citations,
March 2023 in “bioRxiv (Cold Spring Harbor Laboratory)” Dynamic, light touch is sensed through a common mechanism involving Piezo2 channels in sensory axons.
1 citations,
February 2024 in “npj digital medicine” Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
1 citations,
November 2020 in “bioRxiv (Cold Spring Harbor Laboratory)” Quantifying hair shape is better than using racial categories for understanding hair characteristics.
Proretinal nanoparticles are a safe and effective way to deliver retinal to the skin.
8 citations,
August 2021 in “Computational and Mathematical Methods in Medicine” Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
8 citations,
January 2022 in “Sensors” Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
4 citations,
October 2022 in “Journal of Imaging” An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.
1 citations,
December 2022 in “JAMA Dermatology” The AI system HairComb accurately scores hair loss severity, matching dermatologist assessments.
128 citations,
December 2006 in “Journal of Biological Chemistry” Altering SSAT affects fat metabolism and body fat in mice.
15 citations,
January 2023 in “Biomaterials Research” 3D bioprinting in plastic surgery could lead to personalized grafts and fewer complications.
2 citations,
July 2023 in “Agronomy” Melatonin helps wheat plants resist drought by improving their root and root hair growth.
September 2024 in “Journal of Investigative Dermatology” A new tool can analyze hair to detect changes due to hormones, genetics, and aging.
50 citations,
December 2011 in “Skin Research and Technology” The algorithm effectively removes hair from skin images, improving melanoma diagnosis accuracy.
9 citations,
January 2020 in “IEEE Access” The KEBOT system is a highly accurate AI tool for analyzing hair transplants.
December 2021 in “Acta dermato-venereologica” A deep learning model accurately predicts male hair loss types using scalp images.
21 citations,
September 2008 in “Magnetic Resonance Imaging” MRI can effectively image skin structures noninvasively.
January 2024 in “Lecture notes in networks and systems” "TRICHOASSIST" is a system that analyzes hair and scalp images to help diagnose scalp diseases.
9 citations,
January 2011 in “Skin Research and Technology” The new automatic tool accurately measures hair thickness and is reliable.
18 citations,
September 2013 in “Technology” The study introduced a new imaging technology to track skin healing and bone marrow cell activity over time.