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Application and progress of artificial intelligence in radiation therapy dose prediction.
Jiang, Chen; Ji, Tianlong; Qiao, Qiao.
Afiliación
  • Jiang C; Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China.
  • Ji T; Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China.
  • Qiao Q; Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China.
Clin Transl Radiat Oncol ; 47: 100792, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38779524
ABSTRACT
Radiation therapy (RT) nowadays is a main treatment modality of cancer. To ensure the therapeutic efficacy of patients, accurate dose distribution is often required, which is a time-consuming and labor-intensive process. In addition, due to the differences in knowledge and experience among participants and diverse institutions, the predicted dose are often inconsistent. In last several decades, artificial intelligence (AI) has been applied in various aspects of RT, several products have been implemented in clinical practice and confirmed superiority. In this paper, we will review the research of AI in dose prediction, focusing on the progress in deep learning (DL).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Transl Radiat Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Clin Transl Radiat Oncol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Irlanda