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This study used machine learning models, such as Convolutional Neural Networks (CNN), to accurately differentiate False Daisy from similar plants like Smooth Joyweed.

In this retrospective study, researchers developed machine learning models to predict mortality risk among 7115 COVID-19 patients in Iran, finding that the random forests model performed best with 96% accuracy and identified factors like intubation and SpO2 as significant predictors.

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 a data-driven model using XGBoost effectively predicts individualized responses to minoxidil for androgenetic alopecia, outperforming traditional methods in accuracy and reliability.

This study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

This study developed a 3D bioprinted skin model to mimic pemphigus vulgaris, providing a tool to study disease mechanisms and test targeted therapies by reproducing the architecture and pathogenic disruptions of native skin.

This study found that cell-free RNA, particularly DNAJB9, shows potential as a biomarker for diagnosing and prognosing female androgenetic alopecia using a machine learning model.

This research introduces WomenCare, a web-based system using a machine learning model to predict PCOD risk by evaluating factors like age, BMI, and lifestyle habits; it aims to help women monitor their health but is not a substitute for a professional diagnosis.

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.

This study observed distinct plasma miRNA profiles in alopecia areata, identifying several miRNAs downregulated in both mild and severe forms; machine learning models demonstrated strong predictive accuracy, and kinase inhibitors were suggested as promising therapeutic targets based on pathway analysis.

In this review, the authors examined the role of the extracellular matrix in cancer progression and highlighted how decellularization techniques are used to create biomimetic tumor models, discussing the potential for personalized and predictive cancer treatments using advanced biomaterials combined with AI and machine learning.

This study reported that machine learning analysis of protein profiles in hair segments achieved high accuracy in distinguishing women with non-suicidal self-injury disorder from healthy controls, suggesting hair proteomics as a promising non-invasive biomarker for stress-related psychopathology with potential clinical applications.

In this study, researchers introduced a machine learning approach to discover new nanozymes through the DiZyme platform, enabling the accurate prediction of multiple catalytic activities, and providing a comprehensive database and assistant resources for users.

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 introduces a novel computational model using fuzzy logic and multi-criteria decision-making techniques to create a triage system for androgenetic alopecia management, stratifying patients into seven severity levels and aiding in resource allocation and treatment planning.

In this study, researchers identified six novel prognostic biomarkers for bladder cancer and developed a predictive model that effectively stratifies patients into high-risk and low-risk groups based on immune cell infiltration differences and gene expression.

The researchers reported that repurposing the drug eflornithine may offer a treatment option for Bachmann-Bupp Syndrome, highlighting a potential model for other rare diseases.

This study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

This study found that spaceflight may induce skin health risks in astronauts by causing DNA damage and mitochondrial dysregulation, while also identifying gene expression changes that help re-adaptation after returning to Earth.

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

In this study, researchers used Raman spectroscopy to identify melanin-specific features in mouse hair as potential biomarkers for gamma-radiation exposure, achieving a sensitivity of 88% and specificity of 83%, with classification accuracy declining over time beyond 7 days post-irradiation.

This study identified key genes linked to immune cells and potential therapeutic compounds for alopecia areata by evaluating upregulated genes from patient datasets, highlighting T and NK cell involvement in hair follicle attack and suggesting drug candidates through molecular docking and dynamics simulations.

This review explores the convergence of functional biomaterials, biosensing, and AI technologies in bioengineering, highlighting applications in cancer modeling and regenerative medicine, while addressing challenges like biofouling and dataset integration.

This research identified unique gut microbiota patterns in alopecia areata patients, with potential as diagnostic biomarkers and suggesting avenues for microbiota-focused therapies.

This study identified a unique circulating microRNA signature associated with severe alopecia areata, distinguishing it from other inflammatory skin conditions and suggesting these miRNAs as non-invasive biomarkers for diagnosis and potential therapeutic targets.

This study identifies gene-regulatory networks related to genetic variants in skin and hair diseases, suggesting that dermal papilla cells are crucial in androgenetic alopecia.

This study found that mutations in the TULP3 gene are associated with progressive degeneration of the liver, kidney, and heart in adults, highlighting the importance of early detection and management.

This review highlights advancements in nanotechnology-based treatments for androgenetic alopecia, noting that these strategies could improve targeting and modulation of hair follicle environments compared to traditional therapies like minoxidil and finasteride, but also emphasizes challenges around safety, manufacturing, and regulation.