후두암에서의 AI(Artificial Intelligence) 적용
Application of Artificial Intelligence (AI) in Laryngeal Cancer
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초록
The application of artificial intelligence (AI) in laryngeal cancer (LC) has the potential to improve accuracy and efficiency in diagnosis and treatment, as well as aid in predicting patient prognosis and personalized therapy. The application of AI in LC has expanded to include various data modalities, enabling support for screening, diagnosis, therapeutic decision-making, and prognosis. This multidimensional approach utilizes the following data modalities: 1) Videomics: AI algorithms can analyze video recordings of laryngeal endoscopy or surgical procedures to aid in LC diagnosis and treatment planning. 2) Radiomics: AI algorithms can extract and analyze quantitative features from medical imaging data, such as computed tomography (CT ) scans and magnetic resonance imaging (MRI), to assist in LC screening and diagnosis. 3) Acoustics: AI-based acoustic analysis techniques can assess vocal characteristics and patterns in LC patients. 4) Clinical data: AI algorithms can integrate clinical data, including patient demographics, medical history, and laboratory results, to support LC diagnosis, staging, and treatment decision-making. 5) Genomics: by integrating genomic data, such as gene expression profiles or DNA sequencing results, with AI algorithms, researchers can identify molecular markers associated with LC. By considering various factors, such as tumor characteristics, stage, and individual patient factors, AI algorithms can provide insights to optimize treatment strategies and improve patient outcomes. However, it is crucial to emphasize that AI should not replace healthcare professionals but rather serve as a supportive tool. Here, published papers on AI applied to the diagnosis, treatment, and prognosis of laryngeal cancer are reviewed and useful information is provided to readers.
Abstract
The application of artificial intelligence (AI) in laryngeal cancer (LC) has the potential to improve accuracy and efficiency in diagnosis and treatment, as well as aid in predicting patient prognosis and personalized therapy. The application of AI in LC has expanded to include various data modalities, enabling support for screening, diagnosis, therapeutic decision-making, and prognosis. This multidimensional approach utilizes the following data modalities: 1) Videomics: AI algorithms can analyze video recordings of laryngeal endoscopy or surgical procedures to aid in LC diagnosis and treatment planning. 2) Radiomics: AI algorithms can extract and analyze quantitative features from medical imaging data, such as computed tomography (CT ) scans and magnetic resonance imaging (MRI), to assist in LC screening and diagnosis. 3) Acoustics: AI-based acoustic analysis techniques can assess vocal characteristics and patterns in LC patients. 4) Clinical data: AI algorithms can integrate clinical data, including patient demographics, medical history, and laboratory results, to support LC diagnosis, staging, and treatment decision-making. 5) Genomics: by integrating genomic data, such as gene expression profiles or DNA sequencing results, with AI algorithms, researchers can identify molecular markers associated with LC. By considering various factors, such as tumor characteristics, stage, and individual patient factors, AI algorithms can provide insights to optimize treatment strategies and improve patient outcomes. However, it is crucial to emphasize that AI should not replace healthcare professionals but rather serve as a supportive tool. Here, published papers on AI applied to the diagnosis, treatment, and prognosis of laryngeal cancer are reviewed and useful information is provided to readers.
참고문헌 (22)
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