Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of
consensus on how to assess and validate auto-contouring systems currently limits clinical …

Introduction The aim of this review is to describe how various AI-supported applications are
used in head and neck cancer radiotherapy treatment planning, and the impact on dose …

Introduction Organ-at-risk segmentation for head and neck cancer radiation therapy is a
complex and time-consuming process (requiring up to 42 individual structure, and may …

Background Fast and accurate multiorgans segmentation from computed tomography (CT)
scans is essential for radiation treatment planning. Self‐attention (SA)‐based deep learning …

Background and purpose Automation in radiotherapy treatment planning aims to improve
both the quality and the efficiency of the process. The aim of this study was to report on a …

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To
design a personalized treatment with reduced radio-induced toxicity, accurate delineation of …

Over the last decades, the field of medicine has witnessed significant progress in artificial
intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems …

Background MR-Linac allows for daily online treatment adaptation to the observed geometry
of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer …

Background and purpose We proposed an artificial neural network model to predict
radiobiological parameters for the head and neck squamous cell carcinoma patients treated …

Artificial intelligence (AI) is a rapidly expanding field of computer and data science
encompassing a wide range of technologies that aim to emulate the decisionmaking …