摘要以顺铂为基础的全身化疗联合外照射序贯腔内近距离放射治疗(ICBT)已成为局部晚期宫颈癌的标准治疗模式。得益于医学影像设备成像精度的提高和图像融合技术的发展，ICBT已向图像引导的近距离治疗(IGBT)发展，并已走出了仅仅依赖单一影像引导的模式。如何选择适合的影像采集技术、优化多模态成像融合策略以降低IGBT的剂量偏差等因素是决定宫颈癌治疗成败的关键，也是困扰放疗实践的重要因素。基于深度学习的人工智能技术在智能放疗平台搭建及解决方案中崭露头角，已成为解决多模态融合宫颈癌IGBT关键问题的重要抓手，同时也为提升宫颈癌区域整体诊疗水平、减轻医师工作负担、向基层单位推广放疗经验提供一条新途径。

abstractsCisplatin-based systemic chemotherapy combined with external beam radiation followed by intracavitary brachytherapy (ICBT) has become the standard treatment modality for locally advanced cervical cancer. Benefiting from the improvement in the imaging accuracy of medical imaging equipment and the development of image fusion technology, ICBT has developed into image-guided brachytherapy (IGBT) rather than the mode relying only on single image guidance. Factors such as the selection of a suitable image acquisition technology and the optimization of the multimodal imaging fusion strategy to reduce the dose deviation of IGBT are the key to the success of cervical cancer treatment. Radiotherapy practice is also plagued by these factors. Deep learning-based artificial intelligence technology has emerged in constructing intelligent radiotherapy platforms and solutions and has become an important means of solving the key problems in the multi-modal fusion IGBT for cervical cancer. Moreover, this technology is also a new way to improve the overall diagnosis and treatment level of cervical cancer, reduce the workload of physicians, and popularize the radiotherapy experience in grassroots organizations.