March 2026 in “Journal of Translational Autoimmunity” In this study using a mouse model with alopecia areata, the researchers found that ruxolitinib enhances hair regrowth by inhibiting the JAK-STAT signaling pathway, reducing inflammation and oxidative stress, and decreasing apoptosis, which supports its clinical effectiveness in treating this condition.
February 2024 in “Frontiers in physics” This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.
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.