Copyright
©The Author(s) 2021.
Artif Intell Med Imaging. Apr 28, 2021; 2(2): 13-31
Published online Apr 28, 2021. doi: 10.35711/aimi.v2.i2.13
Published online Apr 28, 2021. doi: 10.35711/aimi.v2.i2.13
Table 3 Radiotherapy planning
Ref. | Aim | Patient number | Artificial intelligence technique | Results |
Zhu et al[61], 2020 | Calculating TERMA and ED | 24 | CNN | 3%/2 mm, 95% LCL, and 95% UCL to 99.56%, 99.51%, 99.61% |
Zhang et al[62], 2020 | Making voxel level dose estimation by integrating the distance information between PTV and OAR | 98 | DCNN | MAEV: (1) PTV: 2.1%; (2) Left lung: 4.6%; (3) Right lung: 4.0%; (4) Heart: 5.1%; (5) Spinal cord: 6.0%; and (6) Body: 3.4% |
Fan et al[64], 2019 | Developing a 3D dose estimation algorithm | 270 | Significant difference was found between the estimated and the actual plan in only PTV70.4 | |
Ma et al[65], 2019 | Creating a DVH prediction model | 63 | SVR | The error limit of 10% for the bladder and rectum was 92% and 96% |
Mahdavi et al[69], 2015 | Selecting treatment beam angles in thoracic cancers | 149 | ANN | The majority of plans (93%) were approved as clinically acceptable by three radiation oncologists |
- Citation: Yakar M, Etiz D. Artificial intelligence in radiation oncology. Artif Intell Med Imaging 2021; 2(2): 13-31
- URL: https://www.wjgnet.com/2644-3260/full/v2/i2/13.htm
- DOI: https://dx.doi.org/10.35711/aimi.v2.i2.13