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This study shows that multiphoton microscopy can non-invasively distinguish scarring from non-scarring alopecia by identifying specific morphological features in hair follicle structures.

This study found that combining dermoscopy with reflectance confocal microscopy is more sensitive and specific than using either method alone for assessing vitiligo disease activity and treatment response. Additionally, specific characteristics observed via these techniques correlated with either good or poor treatment outcomes.

This study found that Imaging Mass Cytometry effectively visualizes multiple biomarkers in alopecia areata, enhancing analysis of immune cell and tissue interactions in hair pathology.

This study proposes a new method for hair segmentation that improved performance in skin lesion images, as indicated by an increase in the Dice score from 76.97% to 79.08%.

Confocal Laser Scanning Microscopy (CLSM) is a useful tool for studying how drugs interact with skin and diagnosing skin disorders, despite some limitations.

This study found that Raman spectroscopy has potential for detecting biophysical differences in basal cell carcinoma compared to normal skin structures, supporting its future use in Mohs surgery.

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.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

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.

This study suggests that using UVFD to examine non-pigmented facial lesions can potentially enhance diagnostic accuracy and reduce unnecessary biopsies.

This preliminary study suggests that Raman micro-spectroscopy can help differentiate basal cell carcinoma from hair follicles in skin tissue sections, although some misclassification of hair follicles as carcinoma was observed.

This study shows that multiphoton microscopy can non-invasively distinguish between scarring and non-scarring alopecia by identifying certain morphological features, but further research is needed to confirm its clinical utility.

This review outlines how dermoscopy enhances diagnostic accuracy by up to 30% compared to unaided visual inspection and explores its potential as a research tool without presenting new research findings.

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.

This article describes the immunofluorescence tomography method to achieve high-resolution 3-D reconstruction of epithelial tissues and reports no clinical results.

In this preclinical study, researchers developed and evaluated a new non-invasive laser system designed for personalized medical use, showing its potential for chronic disease management and adjunctive fat reduction by offering enhanced treatment precision and adaptability over existing devices.

This review discusses recent advances in noninvasive hair imaging technologies, such as global photography, trichoscopy, reflectance confocal microscopy, and optical coherence tomography, highlighting their potential to improve hair diagnostics and treatment monitoring, while also outlining their clinical applications and limitations.

This abstract presents supplementary materials illustrating trichoscopy in alopecia areata and scarring alopecia using different dermoscopy techniques but reports no clinical findings.

This study introduces a new intracavity frequency modulation technique for tunable picosecond optical parametric oscillators, enhancing chemical contrast in coherent Raman imaging by allowing real-time subtraction of background signals.

This study introduces scINSIGHT, a method that showed improved performance over existing approaches in identifying gene expression patterns and cellular processes in heterogeneous scRNA-seq datasets from different biological conditions.

This abstract presents supplementary trichoscopy images for alopecia areata and scarring alopecia but reports no clinical findings.

This study presented a new algorithm and camera-based system that successfully reconstruct high-quality, 3D representations of facial hair and underlying skin surfaces even in dense regions like eyebrows.

This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.

This study demonstrates that the newly developed portable reflectance confocal microscope (PRCM) offers real-time, noninvasive monitoring of wound healing processes by visualizing skin morphology in both mice and humans.

In this study, researchers used Raman spectroscopy to identify melanin-specific features in mouse hair as potential biomarkers for gamma-radiation exposure, achieving a sensitivity of 88% and specificity of 83%, with classification accuracy declining over time beyond 7 days post-irradiation.

This paper reports on various AI and human augmentation technologies being applied in plant biology to enhance data processing, improve research efficiency, and enable the discovery of complex biological phenomena that are challenging for humans to perceive or quantify unaided.

This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.