RESUMO
Objective: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.