Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

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 hair loss is common among patients with inflammatory bowel disease, with mesalamine and anti-TNF medications associated with lower odds of experiencing hair loss.

This article discusses the association of stressful life events with alopecia areata and their effects on patients' psychosocial outcomes, but it reports no new empirical findings.

This article provides a comprehensive overview of tattoo-related issues for clinicians, covering various aspects such as tattoo types, medical relevance, removal methods, complications, and South African legislation, but reports no new research findings.

This letter discusses the high prevalence and limited treatment options for pattern hair loss, noting the associated psychological distress, especially in women, and reports no new clinical findings.

This review outlines the current regulatory framework for cosmetics under the FD&C Act, highlighting that they are less strictly regulated than drugs, but notes potential future changes due to proposed legislations requiring stronger safety measures.

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%.

Atopic dermatitis severity doesn't worsen during pregnancy for most women.

This study reports on a new semantic annotation approach used to identify substances in MEDLINE abstracts responsible for adverse drug reactions, with promising performance shown by a prototype system.

This study identified a moderately high prevalence of PCOS and significant associations with factors like hyperprolactinemia and menstrual irregularities in the sample, emphasizing the importance of awareness and early diagnosis to potentially reduce PCOS burden.

This study by Rucker Wright et al. found that hair care practices, particularly traction on relaxed hair, are associated with an increased risk of traction alopecia in African American girls.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

This paper compiles previously observed similarities in biomagnetic fields emitted by human hair and mouse vibrissa follicles, with findings supporting distinctive patterns of biomagnetic activity skewed to one side.

This study found that a high proportion of alopecia areata patients experienced a relapse, typically within one year, with more extensive initial scalp involvement predicting an earlier return of the disease.

Winter-onset alopecia areata patients are more likely to regrow hair within a year compared to spring-onset patients.

This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.

Increased sunscreen use may be linked to frontal fibrosing alopecia in Hispanic females.

This study presents a new survey instrument that was cognitively tested for understanding menstrual cycle characteristics and androgen excess, including hirsutism, alopecia, and acne.

This study evaluated an online survey tool designed to identify polycystic ovary syndrome, identifying areas for improvement that can aid in accurately assessing PCOS prevalence in general populations.

This study reports that the Swedish newborn screening program for phenylketonuria, galactosaemia, and biotinidase deficiency is effective, with high sensitivity and specificity, and lower false positive rates compared to other countries, while genetic variants impact detection and incidence patterns in Sweden.

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.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This review discusses the occurrence of persistent sexual, physical, neurological, and psychiatric side effects known as post-finasteride syndrome after 5x-reductase inhibitor treatment, and emphasizes the importance of evaluating these risks for patients with benign prostatic hyperplasia or androgenic alopecia.

This case study describes an 85-year-old man's intermittent fever being ultimately diagnosed as Babesia infection after considering his travel history and diagnosing splenic infarcts, highlighting the importance of thorough patient history for accurate diagnosis.

This study found that individuals with premature graying of hair in India had significantly lower levels of serum ferritin, vitamin B12, and vitamin D3 compared to controls.

This study found that a family history of androgenetic alopecia and specific trichoscopic signs are strong predictors of female pattern hair loss, leading to a nomogram model for risk prediction.

This study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

This study documented that a CNN-KNN hybrid model achieved 98% accuracy in predicting hair breakage levels due to Telogen Effluvium, highlighting its potential for enhancing diagnosis and treatment in clinical dermatology through early detection of hair-related conditions.

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

Age, diabetes, and cardiogenic shock at PCI are key factors linked to in-hospital death in STEMI patients with hypertension.