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<p>Hair loss, medically known as alopecia, has a profound effect on the psychological disposition of those who suffer from it, making it a topic of great importance to improve treatment and understand hair biology. This paper will look into baldness’s biological and genetic causes in the quest to give an all-inclusive overview of the classifications and detection techniques. A note on the conventional treatments, including Minoxidil and Finasteride, and innovative therapies, like platelet-rich plasma and stem cell therapy, is also considered. Moreover, the paper discusses technological advancements that play an essential role in early diagnosis, including scalp dermoscopy, trichoscopy, and digital imaging. The rationale behind using machine learning (ML) techniques like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Self-Regulated Networks like RegNet-64 is their automation capacity in baldness detection and classification. A comparative analysis of these models has further disclosed and revealed their strengths and weaknesses in both clinical and research settings. This review of the prior literature will emphasize the mighty role of Artificial Intelligence (AI) and ML in improving diagnostic precision, facilitating timely interventions, and enhancing patient outcomes in managing hair loss. Comparative analysis of such models has revealed more and further disclosed and exposed their strengths and weaknesses in both clinical and research settings. In this review of earlier literature, the mighty role of AI and ML will be highlighted to improve diagnostic precision, allowing for timely interventions and, thus, improved patient outcomes in managing hair loss.</p>

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Innovative approaches to baldness detection and management with artificial intelligence and machine learning

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Madhavi Dachawar,
  • Sunitha Sampathi,
  • Vikrant Vijaykumar Ladkat,
  • Vedant Bhalchandra Tong,
  • Vinit Bharat Gaikwad,
  • Omkar Eknath Bhagwat

摘要

Hair loss, medically known as alopecia, has a profound effect on the psychological disposition of those who suffer from it, making it a topic of great importance to improve treatment and understand hair biology. This paper will look into baldness’s biological and genetic causes in the quest to give an all-inclusive overview of the classifications and detection techniques. A note on the conventional treatments, including Minoxidil and Finasteride, and innovative therapies, like platelet-rich plasma and stem cell therapy, is also considered. Moreover, the paper discusses technological advancements that play an essential role in early diagnosis, including scalp dermoscopy, trichoscopy, and digital imaging. The rationale behind using machine learning (ML) techniques like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Self-Regulated Networks like RegNet-64 is their automation capacity in baldness detection and classification. A comparative analysis of these models has further disclosed and revealed their strengths and weaknesses in both clinical and research settings. This review of the prior literature will emphasize the mighty role of Artificial Intelligence (AI) and ML in improving diagnostic precision, facilitating timely interventions, and enhancing patient outcomes in managing hair loss. Comparative analysis of such models has revealed more and further disclosed and exposed their strengths and weaknesses in both clinical and research settings. In this review of earlier literature, the mighty role of AI and ML will be highlighted to improve diagnostic precision, allowing for timely interventions and, thus, improved patient outcomes in managing hair loss.