2 citations
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January 2024 In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.
5 citations
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June 2023 in “Engineering Technology & Applied Science Research” This study developed a new neural network model (AA-GAN-AB-MTEDeep) to enhance Alopecia Areata classification using synthetic scalp images, achieving an accuracy of 96.94%.
7 citations
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October 2023 in “Journal of Intelligent & Fuzzy Systems” This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.
39 citations
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March 2022 in “Infection” Many recovered COVID-19 patients in Saudi Arabia still experience symptoms like fatigue and anxiety, especially older adults and those with other health issues.
204 citations
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May 2014 in “The Journal of Sexual Medicine” This study concluded that current cross-sex hormone treatments for trans men and trans women are effective with a low risk of side effects in the short term, though they did observe specific clinical changes.