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Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm
Peiru Liu1,2, Ying Sun1, Xinzhuo Zhao3
1General Hospital of Northern Theater Command, Department of Radiation Oncology, Shenyang, China.
Biomedical Engineering Online
|November 2, 2023
Summary
Deep learning (DL) algorithms automate organ at risk (OARs) contouring in head and neck cancer radiotherapy, significantly improving accuracy and efficiency. This systematic review confirms DL
Area of Science:
- Radiotherapy
- Medical Imaging
- Artificial Intelligence
Background:
- Organ at risk (OARs) contouring is critical but time-consuming in head and neck cancer radiotherapy planning.
- Deep learning (DL) shows promise for automating this complex process.
Conclusions:
- DL-based automated contouring is essential for head and neck OARs, enhancing accuracy and reducing workload.
- DL facilitates personalized, standardized, and precise radiotherapy.
- Future improvements depend on high-quality datasets and algorithm optimization.
