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In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

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.

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 high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.

This study found that encapsulating dermal papilla spheroids in alginate hydrogel with extracellular matrix proteins increased alkaline phosphatase activity, suggesting an enhanced manipulation of dermal papilla activity.

This study explored social media users' attitudes towards cosmetic and lifestyle products, revealing mixed perceptions, including positive views on rosemary and Sudocream for certain benefits, but also negative experiences like hair loss and worsening acne.

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 found that combining Hebai Formula Granule Wash with minoxidil tincture was more effective than minoxidil alone in treating androgenic alopecia, as it significantly improved clinical efficacy, promoted new hair growth, and reduced hair loss in patients.

In this study, adding plum blossom needle tapping to finasteride treatment significantly improved the clinical efficacy and symptom scores for male patients with androgenic alopecia compared to finasteride alone.

This study found that adding the Liangxue Zhituo prescription to a 5% minoxidil treatment resulted in a significantly higher improvement rate for patients with androgenic alopecia compared to minoxidil alone, with no significant adverse reactions reported.

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 study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.

In this study, researchers aim to use AI-related methods to predict different hair loss patterns, including male and female pattern baldness, alopecia areata, telogen effluvium, and traction alopecia, though specific results are not reported.

This study found that minimally invasive combined fascia sheath suspension improved eyelid symmetry in patients with unilateral ptosis, particularly in measures of marginal reflex distance and eyelid contour.

This study describes the development and testing of an IoT-based smart pot system designed for optimal lavender plant care, utilizing temperature, pH, and soil type monitoring via a Node MCU to control watering based on soil moisture levels.

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

This paper introduces the first computational model of male baldness that simulates hair loss using the Hamilton-Norwood classification and has been deemed plausible for clinical use by a dermatologist.

This study found that chemical hair dyes caused more significant microstructural damage to hair than high-temperature perms, as observed using a single-detector OCT system.

The researchers developed a new follicular unit extraction system with adjustable needle lengths and precise rotation speed, which they report as being superior to existing systems for non-incisional hair transplants.

This study found a direct correlation between IL6 and insulin, glucose, and cholesterol serum levels in patients with oral lichen planus, suggesting a potential increased cardiovascular risk.

This laboratory study observed that Prunus Tomentosa Thumb Total Flavone significantly promoted hair growth and follicle maturation in a C57BL 6 mouse model of hair loss.

This study observed an increase in hair thickness and reduced hair loss in patients with androgenetic alopecia after treatment with autologous cellular suspension using the Rigenera® system, though further research is needed.

This case report observed a dramatic improvement in a 15-year-old girl's pachyonychia congenita symptoms during chemotherapy for Ewing sarcoma, suggesting chemotherapy's potential role in managing hyperkeratotic conditions.

This study assessed the potential for Ocean Thermal Energy Conversion power in Fiji, finding higher power output and efficiency during summer due to greater temperature differences between surface and deep sea waters.

This study tested a folate-conjugate drug delivery system and found its cytotoxicity depends on whether the treated cells overexpress folate receptors, suggesting potential for targeted chemotherapy.

This research article evaluates the protein content of yeast grown on wood hydrolyzate, without providing new results or clinical findings.

This study found that expression of GDNF and GFR α-1 proteins in human skin decreases with age, particularly in the epidermis, suggesting that their signaling pathways may be altered as people grow older.