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This study found that applying transfer learning with CNN architectures like AlexNet, VGG16, and ResNet50 achieved 99% accuracy in classifying multiclass hair disorders, suggesting a potential technological aid for dermatologists in diagnosing and treating hair conditions.

This study introduces a novel method for capturing and digitally rendering the color appearance of physical hair samples using deep neural networks.

AI can accurately measure hair loss severity in alopecia areata.

This article discusses the increasing trend of retargeting existing drugs for new indications and emphasizes the need for additional support in drug development, without reporting specific new results.

In this study, researchers developed an AI-powered platform called VitaDetect, which screens for vitamin deficiencies using image analysis of nails, tongue, and skin, aiming to provide an accessible and early-stage detection tool in resource-limited settings.

This study presents a new automated computer vision system to objectively measure periocular hair density changes in breast cancer patients undergoing chemotherapy, demonstrating high precision in tracking individual changes and potential as a reliable tool for future clinical trials.

This research by Yuan et al. focused on developing a comprehensive human skin cell atlas, analyzing various cell types and diseases, and introduced a deep learning method, scSEA, for unbiased reference mapping, potentially discovering new cell types.

The researchers developed a comprehensive human skin cell atlas using data from various studies and established a consensus nomenclature for normal human skin in this project, which also includes a deep learning-based method for more effective reference mapping of new cells.

In this study, a deep learning model using an optimized VGG19 architecture achieved a high classification accuracy of 98.64% for detecting ten hair disease classes from a balanced dataset, indicating its potential for reliable use in mobile diagnostics for clinical and remote applications.

This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

In this research, experienced clinicians noted inconsistency in the reproducibility of the commonly used Hamilton-Ludwig scales for assessing pattern hair loss severity in photographic assessments, leading to the proposal of a new 5-point scale for staging female hair loss.

This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

This study highlights the need for personalized hair care plans based on scalp type and environmental factors, underscoring the importance of targeted product selection and the involvement of professionals to create effective solutions for modern lifestyle issues.

This study identified three genetic loci influencing hair density in East Asian populations and found associations with demographic and lifestyle factors like age, sex, and BMI. The results also suggest possible genotype-specific responses to finasteride for managing hair disorders.

This study developed a deep learning-based method that identified 3,620,516 potential drug-disease associations, suggesting a promising tool for large-scale virtual screening in drug research.

In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.

This study introduces the Hair Cell Analysis Toolbox (HCAT), a machine-learning software that automates the analysis of cochlear hair cells, enabling unbiased and comprehensive imaging data interpretation.

This study found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.

This study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.

This study found that real-time ultrasound-assisted gluteal fat grafting effectively allowed surgeons to verify subcutaneous fat injection, potentially reducing risks of major complications, though it involved increased costs, surgical time, and a learning curve.

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 review found that AI in dermatology shows high diagnostic accuracy comparable to experienced clinicians, but integration into clinical practice faces challenges requiring further research.

This study presents an economical and user-friendly training module using common materials and goat skin to teach the steps of the strip method for hair follicle harvesting and implantation.

This study found that gelatin sponges used as scaffolds in rats with deep wounds and periosteal defects enabled regeneration of diverse tissue types, including periosteum, skin, and appendages, highlighting the role of vascular niche formation.

This case report describes a patient with Birt-Hogg-Dube syndrome exhibiting multiple fibrofolliculomas, acrochordons, and renal oncocytoma.

The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.

Machine learning is effective in predicting gene functions and their relationships with diseases.

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

In this study, adipose-derived stem cells from superficial adipose tissue showed higher regenerative potential, and more macrophages were found in superficial than deep adipose tissue, potentially influenced by skin microbiota.