September 2025 in “Diseases” This study found that patients with patchy alopecia areata had significantly higher serum levels of pro-inflammatory cytokines IL-6, TNF-α, IL-17A, and IL-21 compared to healthy controls, with these levels correlating positively with disease severity, age, and disease duration.
May 2026 in “International Journal of Scientific Research in Science and Technology” This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.
5 citations
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December 2022 in “arXiv (Cornell University)” This study used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.
February 2025 in “Skin Research and Technology” This study highlights the potential of novel non-invasive testing techniques to enhance the diagnosis, treatment, and care of scalp hair diseases, urging future research to improve their accuracy and efficiency.
3 citations
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January 2023 in “European Journal of Information Technologies and Computer Science” This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.