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This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This supplementary issue of the Pakistan Journal of Urology contains diverse studies spanning organ donation's significance, surgical techniques, and the comparison of treatments in urology, but it doesn't provide specific research results or detailed findings.

This survey paper analyzes advancements in robotic surgery, highlighting the integration of autonomous systems in medical practices and their impact on precision and patient outcomes, while discussing benefits, challenges, and ethical considerations, with a focus on applications from neonatal to neurosurgical procedures.

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 research examined the transcriptional landscape of quiescent melanocyte stem cells (qMcSCs) in adult female mice, revealing significant heterogeneity within this cell population and identifying novel subpopulations that vary in immune privilege regulation, melanocyte differentiation potential, and neural crest potential.

This study constructed a comprehensive atlas of prenatal human skin, revealing that innate immune cells, such as macrophages, play a crucial role in skin morphogenesis by interacting with non-immune cells, influencing hair follicle formation and angiogenesis beyond their traditional immune functions.

This review reports various ethnomedicinal uses, phytochemical components, and potential pharmacological effects of Eclipta alba, highlighting benefits like wound healing and neuroprotection, though it underscores the need for further mechanism-based studies and clinical trials.

This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

This study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.

Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

This study introduced a single-stage network using large kernel attention that effectively restores high-resolution images by capturing both global and local details, reducing parameters and improving processing speed.

This thesis presents research conducted in partial fulfillment of a Master's degree in Software Systems Engineering but does not report new empirical findings.

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 developed a novel multi-scale image descriptor using dictionaries for classifying histological images, achieving average recall and precision measures of 0.81 and 0.86 in identifying specific skin structures and pathologies.

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

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 observed that using the LMNN algorithm improved diagnostic accuracy in identifying biomarker correlations associated with hair loss, suggesting potential for advanced automated diagnostics.

This study proposed a model that accurately predicts skin condition using genotype information and machine learning, suggesting potential for creating customized cosmetics.

This study identified a monoclonal antibody, HKN-2, that recognizes specific cells in human skin and may indicate a common antigenic determinant between hair and other skin epithelial tissues.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.

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.

This study found that the multi-thresholding method was the most effective for segmenting hair during laser removal, as it clearly distinguished hair patterns with minimal noise.

This study used machine learning models, such as Convolutional Neural Networks (CNN), to accurately differentiate False Daisy from similar plants like Smooth Joyweed.

This study presents a machine learning algorithm that achieved 89.5% accuracy in predicting hair health using factors like spatial-temporal images, age, and gender.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

This study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.