摘要目的:探讨密集视频描述的自动化良性软组织肿瘤切除术手术记录生成方法及应用价值。方法:应用Transformer深度学习模型建立自动化手术记录生成系统，回顾性分析2021年9月至2023年8月空军军医大学西京医院骨科收治的30例良性软组织肿瘤患者手术视频。将患者数据按照8∶1∶1的比例随机分为训练集、验证集和测试集。在测试集上采用BLEU-1、BLEU-2、BLEU-3、BLEU-4、Meteor、Rouge、CIDEr共7个评价指标对模型生成手术记录文本质量进行评估，并与视频密集描述领域的经典算法并行解码的密集视频描述方法(PDVC)进行对比。结果:该手术记录自动生成系统在测试集中运行的结果：BLEU-1、BLEU-2、BLEU-3、BLEU-4、Rouge、Meteor、CIDEr分别为16.80、15.23、13.01、11.68、16.01、12.67、62.30。经典算法PDVC的运行结果：BLEU-1、BLEU-2、BLEU-3、BLEU-4、Rouge、Meteor、CIDEr分别为15.63、14.17、11.90、10.45、12.97、11.99、53.64。本研究提出的方法所有指标均较PDVC有明显提升，BLEU-4、Rouge、Meteor、CIDEr分别提升了1.23、3.04、0.68、8.66，证明模型可以更好地抓取视频中的关键信息，有助于生成更有效的文本记录。结论:基于密集视频描述的自动化良性软组织肿瘤切除术手术记录生成方法表现出良好的性能。

abstractsObjective:To explore the feasibility and application value of an automated method for generation of surgical records for resection of benign soft tissue tumor based on dense video descriptions.Methods:The Transformer deep learning model was used to establish an automated surgical record generation system to analyze the surgical videos of 30 patients with benign soft tissue tumor who had been admitted to Department of Orthopedics, Xijing Hospital, Air Force Military Medical University from September 2021 to August 2023. The patient data were randomly divided into training sets, validation sets, and test sets in a ratio of 8∶1∶1. In the test sets, 7 evaluation indexes, BLEU-1, BLEU-2, BLEU-3, BLEU-4, Meteor, Rouge, and CIDEr, were used to evaluate the text quality of surgical records generated by the model. The text of surgical records was compared with the classical algorithm, dense video captioning with paralled decoding (PDVC) in the field of video-intensive description.Results:The automated surgical record generation system running in the test sets showed the following: BLEU-1, BLEU-2, BLEU-3, BLEU-4, Rouge, Meteor, and CIDEr were 16.80, 15.23, 13.01, 11.68, 16.01, 12.67 and 62.30, respectively. The operation of the classical algorithm PDVC showed the following: BLEU-1, BLEU-2, BLEU-3, BLEU-4, Rouge, Meteor, and CIDEr were 15.63, 14.17, 11.90, 10.45, 12.97, 11.99 and 53.64, respectively. The automated surgical record generation system resulted in significant improvements compared with PDVC in all evaluation indexes. The BLEU-4, Rouge, Meteor, and CIDEr were improved by 1.23, 3.04, 0.68 and 8.66, respectively, demonstrating that the system proposed can better capture the key data in the video to help generate more effective text records.Conclusion:As the automated surgical record generation system shows good performance in generating surgical records for resection of benign soft tissue tumor based on intensive video descriptions, it can be applied in clinical practice.