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This study found that using 3D total body imaging with convolution neural networks accurately identifies risk phenotypes for melanoma, suggesting improved objective stratification for early detection and prevention.

This study found that UC.145 influences DKK1 methylation and Wnt signaling in gastric cancer, with implications for patient survival and its potential as a predictive biomarker.

This study found that overexpression and knockdown of DKK4 influence genes involved in hair follicle growth and development in Angora rabbits and identified specific SNPs in DKK4 associated with wool quality, notably showing that the TT/GG haplotype combination relates to higher fibre diameters.

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 study used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.

This study found that using machine learning models, particularly Random Forest with 93% accuracy and 86% sensitivity, can effectively predict PCOS by analyzing features like antral follicle count, hair growth, and skin pigmentation, offering a promising alternative to traditional diagnostic methods.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

This review discussed recent research on using machine learning to predict mental disorders, reporting that Adaboost could predict depression with 92.5% accuracy and 93.6% specificity, while other models like XGBoost and RNN were applied for post-stroke depression and EEG-based depression detection, respectively.

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.

The model can effectively identify good quality skin images but needs more testing for real-world use.

This study found that certain gene polymorphisms in keratin 27 and ELOVL4 are linked to improved cashmere fineness and production traits in Liaoning cashmere goats.

This study presents a system for evaluating semantic similarity between sentences by using token-based matching and improved similarity measures beyond WordNet.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

This study used machine learning to identify molecular predictors of drug response in alopecia areata, suggesting a tool for predicting treatment efficacy based on gene signatures.

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

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 applied a machine learning-based genomic analysis to identify genetic markers associated with wool traits in Central Anatolian Merino sheep, successfully highlighting loci relevant to fiber diameter, staple length, and greasy fleece yield, which could inform breeding programs to enhance wool quality and yield.

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

This study found that peanut agglutinin binding patterns after neuraminidase pretreatment can help differentiate malignant melanoma from nevocellular nevus in skin specimens.

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.

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 demonstrated that genetic markers can predict human ear morphology with moderate to good accuracy, potentially aiding forensic identification in crime scene investigations where traditional DNA matches are unavailable.

A deep learning model accurately detects early hair loss signs using scalp images.

This study suggests that novel diagnostic, preventive, and treatment approaches for autoimmune diseases like alopecia areata may be developed by identifying hub genes, and highlights the usefulness of machine learning and bioinformatics in finding new disease biomarkers.

This study found that a random forest algorithm most effectively detected COVID-19, with high specificity and accuracy, among 10,862 individuals in an Iranian hospital setting.

This study developed and validated a radiomics-based clinical nomogram using routine renal ultrasound and clinical data to predict early diabetic kidney injury in a community cohort, finding it significantly outperformed clinical-only models in predicting risk and showed potential as a cost-effective screening tool.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

EfficientNet improves accuracy in diagnosing hair loss stages.