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This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

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.

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

This study developed a deep learning-based method that identified 3,620,516 potential drug-disease associations, suggesting a promising tool for large-scale virtual screening in drug research.

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.

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.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This study found that a lightweight Convolutional Neural Network model can accurately and quickly determine natural hair tone from high-resolution images of hair roots, outperforming other popular methods.

This study proposed a method to automatically detect and replace hair in dermoscopy images, demonstrating high sensitivity and specificity in melanoma diagnostics.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

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.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

This study found that the HairComb algorithm achieved high accuracy in quantifying percentage hair loss across various types of alopecia, suggesting its potential for standardized automated assessments.

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.

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.

This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.

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.

In this study, researchers developed an AI-powered platform called VitaDetect, which screens for vitamin deficiencies using image analysis of nails, tongue, and skin, aiming to provide an accessible and early-stage detection tool in resource-limited settings.

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 developed a method using Convolutional Neural Networks to detect nutritional deficiencies, such as iron, zinc, biotin, and vitamins, through high-resolution images of hair and nails, achieving an 89% accuracy rate in identifying these deficiencies.

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 scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.