基于智能手术流程识别的微创颌下腺切除术技能评估
Intelligent surgical workflow recognition-based skill assessment for minimally invasive submandibular gland resection.
摘要(中文译文)
人工智能（AI）正在改变临床口腔实践，大多数应用集中于放射影像等静态图像。然而，AI在视频数据分析中的整合，尤其是在富含动态信息的外科手术场景中，在口腔医学领域仍未被充分探索。本研究介绍了AI-miSMG，一种为微创下颌下腺（SMG）切除术设计的智能手术工作流识别模型。在我们前期工作的基础上，我们建立了标准化标注方案，以高效标注内镜下SMG切除术流程，该流程分为创建、定位、分离、检查和空闲五个阶段。AI-miSMG模型在包含386122个标注帧的73段高质量内镜下SMG切除术视频数据集上进行了训练。为评估其性能，我们使用了一个多中心数据集，该数据集由来自四个不同中心的手术视频组成，共计85913张图像。该模型在外部验证数据集上达到了0.87的总体准确率。此外，我们还探索了该模型用于基于工作流的不同经验水平术者手术流畅性分析。此外，我们的模型将标注时间减少了约47%，从94.00分钟降至49.90分钟。总体而言，我们开发了一种基于深度学习的微创SMG切除术工作流识别模型，并证明了其用于手术阶段识别、工作流分析和模型辅助标注的可行性。
Abstract
Artificial Intelligence (AI) is transforming clinical dental practice, with most applications focusing on static images like radiographs. However, the integration of AI in analyzing video data, particularly in surgical settings rich in dynamic information, remains underexplored in oral medicine. This study introduces AI-miSMG, an intelligent surgical workflow recognition model designed for minimally invasive submandibular gland (SMG) resection. Building upon our previous work, we established a standardized annotation protocol to efficiently label endoscopic SMG resection procedures, which are divided into Creation, Position, Separation, Inspection, and Idle phases. The AI-miSMG model was trained on a dataset of 73 high-quality endoscopic SMG resection videos, comprising 386,122 labeled frames. To evaluate its performance, we used a multicenter dataset consisting of surgical videos from four different centers, totaling 85,913 images. The model achieved an overall accuracy of 0.87 on the external validation dataset. Additionally, it was explored for workflow-based analysis of surgical fluency across surgeons with different experience levels. Furthermore, our model reduced annotation time by approximately 47%, decreasing from 94.00 min to 49.90 min. Overall, we developed a deep learning-based workflow recognition model for minimally invasive SMG resection and demonstrated its feasibility for surgical phase recognition, workflow analysis, and model-assisted annotation.
如何引用
AMA
Zhongkai Ma, Yufei Hua, Lin Que, Bing Yan, Jialiang Huang, Huawei Ming, et al. Intelligent surgical workflow recognition-based skill assessment for minimally invasive submandibular gland resection.. NPJ digital medicine. 2026; doi:10.1038/s41746-026-02976-w.
APA
Zhongkai Ma, Yufei Hua, Lin Que, Bing Yan, Jialiang Huang, Huawei Ming, et al (2026). Intelligent surgical workflow recognition-based skill assessment for minimally invasive submandibular gland resection.. NPJ digital medicine. https://doi.org/10.1038/s41746-026-02976-w
GB/T 7714
Zhongkai Ma, Yufei Hua, Lin Que, Bing Yan, Jialiang Huang, Huawei Ming, et al. Intelligent surgical workflow recognition-based skill assessment for minimally invasive submandibular gland resection.[J]. NPJ digital medicine, 2026 doi:10.1038/s41746-026-02976-w.
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