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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 found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

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 review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

In this study, researchers developed a method to create a synthetic dataset of facial acne images using generative techniques, achieving 97.6% classification accuracy with InceptionResNetv2, which helps overcome privacy concerns in biomedical applications by using anonymized data.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

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.

This dissertation proposes a framework for analyzing medical images on mobile devices but presents no new research findings, focusing instead on development methodologies and their applications to hair transplant and glaucoma contexts.

This study developed the TLMG hydrogel, a seamless in situ biointerface platform, demonstrating robust adhesion, high conductivity, and therapeutic effects for intelligent wound management in complex animal models and human tests, indicating its promise for integrated bioelectronic medicine.

This study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.

This study proposes a new method for hair segmentation that improved performance in skin lesion images, as indicated by an increase in the Dice score from 76.97% to 79.08%.

This study developed a novel hydrogel that improved healing in diabetic infected wounds on mobile sites by controlling infection, inflammation, and mechanical stress, promoting skin regeneration without excess scarring.

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 study presents a new wireless bioelectronic system powered by ambient Wi-Fi signals that enhances wound healing by providing electrical stimulation and real-time monitoring, offering a promising solution for at-home treatment and regenerative therapy.

This source provides an overview of diagnostic and treatment innovations for gastrointestinal disorders, such as wireless capsule technology for motility assessment and new methods for treating constipation and nausea, highlighting both current and emerging techniques.

This study reports that a novel programmable device delivering VEGF through miniaturized needles significantly improved healing outcomes in diabetic mice's chronic wounds compared to traditional topical treatments.

This study systematically examines how smart biomaterials used with DLP technology can enhance bioprinting in tissue engineering and regenerative medicine, while also identifying current challenges and future research needs.

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.

In this study, human dermal fibroblasts were identified as natural host cells that support productive Merkel cell polyomavirus infection, and the MEK antagonist trametinib was introduced as an effective inhibitor to control the virus.

Smartphone-based teledermatology is effective for diagnosing skin diseases in the military with good accuracy.

This review discusses triboelectric nanogenerator (TENG) devices for body-integrated electrical stimulation therapy and suggests they may revolutionize personalized healthcare, but it reports no new clinical results.

This paper presents a new methodology combining magnetic tweezers and traction force microscopy to study keratinocyte mechanobiology, but reports no experimental results yet.

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

This review highlights the potential of self-powered nanogenerators in revolutionizing biomedical devices by using biomechanical or environmental energy, focusing on applications like regenerative hair growth, drug release patches, and electronic skin while evaluating challenges such as energy efficiency and biocompatibility.