Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

Applied Sciences

Lidia Strigari¹  Giulia Paolani¹  Giuseppe Della Gala¹  Silvia Strolin¹  Miriam Santoro¹  Ilario Ammendolia²  Cinzia Giacometti²  Alessio Giuseppe Morganti²  Alessandro Bartoloni³ 

[1] Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;Department of Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;Istituto Nazionale di Fisica Nucleare (INFN) Sezione di Roma 1, 00185 Roma, Italy;

关键词: artificial intelligence;    radiotherapy;    workflow;    machine learning;    deep learning;    iterative optimization;   

DOI  :  10.3390/app12073223

来源: DOAJ

【 摘 要 】

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.

【 授权许可】

Unknown