Akademik Veri Yönetim Sistemi
Osmangazi Tıp Dergisi, cilt.42, sa.3, ss.339-349, 2020 (Hakemli Dergi)
Abstract: Artificial intelligence (AI) is a computer science that tries to imitate human-like intelligence on machines using computer
software and algorithms without direct human stimuli to perform certain tasks. Machine learning (ML) is the subunit of AI that uses
data-driven algorithms that learn to emulate human behavior based on a previous example or experience. Deep learning (DL) is an
ML technique that utilizes deep neural networks to construct a model. The growth and sharing of data, increased computing power,
and developments in ML have initiated a transformation in healthcare. Advances in radiation oncology have generated substantial
data that must be integrated with computed tomography (CT) imaging, dosimetry, and other imaging modalities before each
fraction. There are many algorithms used in Radiation Oncology. Each of these methods has advantages and limitations and
different computing requirements. In this paper, we aimed to review the radiotherapy (RT) process by identifying the specific areas
in which the quality and efficiency of ML can be increased and a workflow chart can be created. The RT stage is divided into seven
groups as patient assessment, simulation, contouring, planning, quality assessment (QA), treatment application, and patient followup. A systematic evaluation of the applicability, limitations and advantages of ML algorithms was performed at each stage