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Advances in Deep Learning for Head and Neck Cancer: Datasets and Applied Methods.

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Název: Advances in Deep Learning for Head and Neck Cancer: Datasets and Applied Methods.
Autoři: Majeed, Tabasum, Assad, Assif
Zdroj: ENT Updates; 2025, Vol. 15 Issue 1, p1-26, 26p
Témata: MEDICAL care, HEAD & neck cancer, DISEASE risk factors, DEEP learning, ARTIFICIAL intelligence
Abstrakt: Head and neck cancers (HNCs) include malignancies of the oral cavity, salivary glands, thyroid, oropharynx, and nasopharynx, with risk factors such as tobacco use, alcohol consumption, viral infections, and environmental exposures contributing to over half a million global cases annually. Despite treatment advances, poor prognosis underscores the need for accurate diagnosis and continuous monitoring. Medical imaging plays a critical role in HNC evaluation but is often limited by the complexity of anatomy and tumor biology. Recent advances in artificial intelligence (AI), particularly deep learning, offer opportunities to enhance diagnostic accuracy and optimize treatment strategies. This study reviews the application of deep learning in HNC imaging, evaluating different architectures and addressing challenges like limited annotated datasets, high computational demands, and ethical concerns. Overcoming these challenges will revolutionize HNC diagnostics, redefine precision oncology, and improve patient care. The future integration of explainable AI models and multimodal data will be crucial in advancing diagnostic precision, ensuring clinical applicability, and addressing ethical and resource challenges. As AI progresses, its effective integration into clinical workflows will not only enhance healthcare delivery but also reduce inequalities, accelerating significant advancements in HNC management and transforming patient outcomes. [ABSTRACT FROM AUTHOR]
Copyright of ENT Updates is the property of UK Scientific Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Biomedical Index
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  Data: Advances in Deep Learning for Head and Neck Cancer: Datasets and Applied Methods.
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  Data: <searchLink fieldCode="AR" term="%22Majeed%2C+Tabasum%22">Majeed, Tabasum</searchLink><br /><searchLink fieldCode="AR" term="%22Assad%2C+Assif%22">Assad, Assif</searchLink>
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  Data: ENT Updates; 2025, Vol. 15 Issue 1, p1-26, 26p
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  Data: <searchLink fieldCode="DE" term="%22MEDICAL+care%22">MEDICAL care</searchLink><br /><searchLink fieldCode="DE" term="%22HEAD+%26+neck+cancer%22">HEAD & neck cancer</searchLink><br /><searchLink fieldCode="DE" term="%22DISEASE+risk+factors%22">DISEASE risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22ARTIFICIAL+intelligence%22">ARTIFICIAL intelligence</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Head and neck cancers (HNCs) include malignancies of the oral cavity, salivary glands, thyroid, oropharynx, and nasopharynx, with risk factors such as tobacco use, alcohol consumption, viral infections, and environmental exposures contributing to over half a million global cases annually. Despite treatment advances, poor prognosis underscores the need for accurate diagnosis and continuous monitoring. Medical imaging plays a critical role in HNC evaluation but is often limited by the complexity of anatomy and tumor biology. Recent advances in artificial intelligence (AI), particularly deep learning, offer opportunities to enhance diagnostic accuracy and optimize treatment strategies. This study reviews the application of deep learning in HNC imaging, evaluating different architectures and addressing challenges like limited annotated datasets, high computational demands, and ethical concerns. Overcoming these challenges will revolutionize HNC diagnostics, redefine precision oncology, and improve patient care. The future integration of explainable AI models and multimodal data will be crucial in advancing diagnostic precision, ensuring clinical applicability, and addressing ethical and resource challenges. As AI progresses, its effective integration into clinical workflows will not only enhance healthcare delivery but also reduce inequalities, accelerating significant advancements in HNC management and transforming patient outcomes. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of ENT Updates is the property of UK Scientific Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.54963/entu.v15i1.871
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        Text: English
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      – SubjectFull: HEAD & neck cancer
        Type: general
      – SubjectFull: DISEASE risk factors
        Type: general
      – SubjectFull: DEEP learning
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              Text: 2025
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