Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study explored the role of AI technologies in plastic and reconstructive surgery, highlighting their potential across preoperative, intraoperative, and postoperative stages, while also identifying challenges like data privacy and regulatory issues that must be addressed for successful implementation.

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.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This study proposes a new mathematical model, the Harmonic Mean equation, for precisely quantifying nuclear pleomorphism in breast cancer grading, showing high performance with accuracy, recall, specificity, precision, and F1-score metrics.

This article reviews the steps and methods involved in preparing skin tissues for three-dimensional volumetric imaging, highlighting its potential to provide detailed insights into skin structure not possible with traditional 2D histology, but reports no new findings.

This article contains the late-breaking abstracts from the 39th Annual Conference of the American Society for Laser Medicine and Surgery and does not present new research results.

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 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 reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.

Smart microneedles using advanced tech could improve psoriasis treatment.

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

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 using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

This study introduces PROMETHEUS, a framework that organizes causal claims from scientific texts into navigable and persistent "causal atlases," enhancing research by highlighting localized evidence, agreement, and contradictions within complex data sets.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

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.

This study examined the molecular communication in psoriasis cells, highlighting unique immune cell interactions and identifying new features of the hair follicle cell-psoriasis axis. It suggests the potential for targeted therapies at the single-cell level to improve psoriasis treatment.

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

A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.

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.

This article proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

This study found that higher Dietary Inflammatory Index scores were significantly associated with an increase in both the occurrence and severity of alopecia areata.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

This review consolidates various rational design strategies for nanozymes, emphasizing the mechanisms needed for precise design and exploring their applications in treating inflammatory diseases, diagnosing diseases, and environmental uses, while also discussing the challenges and future prospects in this emerging field.

This chapter reviews common cosmetic dermatology techniques but presents no new findings; it discusses treatments like emollients and laser therapy for conditions like photoaged skin and acne scarring.

This study used machine learning to identify FDA-approved drugs afatinib, neratinib, and zanubrutinib as potential KRASG12C inhibitors for resistant non-small-cell lung cancer, highlighting the potential of AI in drug repurposing.