July 2026 in “International Journal of Clinical & Experimental Dermatology” This exploratory pilot study reported that using a 2% Oleosome-FGF-2 fusion extract scalp serum twice daily for 84 days in women with telogen effluvium and brittle hair improved hair density, anagen/telogen ratio, hair shaft thickness, and scalp hydration compared to a placebo.
January 2025 in “Communications in computer and information science” HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.
