Application and progress of artificial intelligence in radiation therapy dose prediction
Jiang Chen, Tianlong Ji, Qiao Qiao
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).
Topics & Concepts
MedicineRadiation doseMedical physicsRadiation therapyModality (human–computer interaction)Clinical PracticeArtificial intelligenceApplications of artificial intelligenceProcess (computing)Computer scienceNuclear medicineFamily medicineSurgeryOperating systemAdvanced Radiotherapy TechniquesRadiomics and Machine Learning in Medical ImagingAdvanced X-ray and CT Imaging