Hairsentinel: A Time-Aware Anomaly Detection Framework for Forecasting Hairfall Trends Using Temporal Fusion Transformers

    October 2025 in “ Frontiers in Artificial Intelligence ”
    A. Anny Leema, T. Saktheshwaran, G. Abitha Sri, P. Balakrishnan
    Studysummary 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.
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    Research cited in this study 11

    1. Leveraging Deep Neural Networks to Uncover Unprecedented Levels of Precision in the Diagnosis of Hair and Scalp Disorders Skin research and technology · 2024
    2. Hair And Scalp Disease Detection Using Deep Learning arXiv (Cornell University) · 2024
    3. Predicting Hair Loss With AI: A Deep Learning Framework Combining Genetic And Scalp Health Data 2024
    4. Female Pattern Hair Loss: An Overview with Focus on the Genetics Genes · 2023
    5. Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and an Ensemble Method International journal on recent and innovation trends in computing and communication · 2023
    6. Hair and Scalp Disease Detection Using Machine Learning and Image Processing European Journal of Information Technologies and Computer Science · 2023
    7. Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks Sensors · 2022
    8. Understanding Pattern Hair Loss: Hair Biology Impacted by Genes, Androgens, Prostaglandins, and Epigenetic Factors Indian Journal of Plastic Surgery · 2021
    9. ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health IEEE Access · 2020
    10. Hair Loss Diagnosis Using Artificial Neural Networks 2012
    11. Burden of Hair Loss: Stress and the Underestimated Psychosocial Impact of Telogen Effluvium and Androgenetic Alopecia Journal of Investigative Dermatology · 2004