| Autor: |
S M H, Luk, E C, Ford, M H, Phillips, A M, Kalet |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Clinical Oncology. 34:89-98 |
| ISSN: |
0936-6555 |
| Popis: |
Radiation therapy is a complex process involving multiple professionals and steps from simulation to treatment planning to delivery, and these procedures are prone to error. Additionally, the imaging and treatment delivery equipment in radiotherapy is highly complex and interconnected and represents another risk point in the quality of care. Numerous quality assurance tasks are carried out to ensure quality and to detect and prevent potential errors in the process of care. Recent developments in artificial intelligence provide potential tools to the radiation oncology community to improve the efficiency and performance of quality assurance efforts. Targets for artificial intelligence enhancement include the quality assurance of treatment plans, target and tissue structure delineation used in the plans, delivery of the plans and the radiotherapy delivery equipment itself. Here we review recent developments of artificial intelligence applications that aim to improve quality assurance processes in radiation therapy and discuss some of the challenges and limitations that require further development work to realise the potential of artificial intelligence for quality assurance. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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Full Text from ScienceDirect