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This study found that the WNMFDDA model effectively predicts drug-disease associations, achieving high accuracy in cross-validation and confirming most candidate associations through existing databases.

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 found that a Faster Residual Convolutional Neural Network model achieved an accuracy of 84.3% in recognizing alopecia areata and various scalp conditions from image databases.

This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

This study found that an ML model incorporating factors like Breslow thickness and age improved cutaneous malignant melanoma prognosis predictions compared to TNM staging, with a C-index of 80% versus 66.6% for TNM alone, suggesting ML's potential for personalized prognostication.

This study found that among various predictive models for baldness risk, Random Forest Regression performed best with the lowest mean squared error and highest R², indicating strong predictive accuracy, especially with complex datasets, while Linear Regression was better suited to simpler datasets.

MultiKano accurately identifies cell types in complex data better than existing methods.

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.

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 study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

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 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, researchers aimed to develop a Naive Bayes algorithm to predict hair loss risk based on personal and clinical data, including age, gender, stress levels, hormones, and family history. Results were not reported.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

In this study, researchers developed a novel image processing method using a multi-class support vector machine that achieved an 89.3% accuracy in classifying alopecia areata and related conditions, outperforming existing models in classification accuracy.

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 introduces Kalya Research, an AI-driven tool designed to identify and categorize literature on complementary and alternative medicines, showing its effectiveness compared to Medline in finding relevant alopecia research within the context of breast cancer patients.

This study found that monoclonal antibodies can identify specific immunological characteristics of hair fibrous proteins, with some antibodies reacting only with hair proteins and others showing broader activity with skin and epithelial cells.

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.

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.

This study developed a leakage-resistant machine learning framework for classifying hair loss types, emphasizing transparency through explainable AI. Among tested models, Extreme Gradient Boosting excelled, achieving high accuracy and stability on both cross-validation and holdout datasets.

This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.

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 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.

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.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

This study found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

This study evaluated a novel, user-friendly approach for detecting hairfall trends over time using machine learning models. The Temporal Fusion Transformer model demonstrated high accuracy in identifying anomalies in hair shedding patterns, potentially aiding in the early detection of health risks related to hormonal fluctuations.