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.

Objective To retrospectively analyze of factors influencing early preterm birth(EPB)and late preterm birth(LPB)in pregnancy women with placenta previa complicated by placenta accreta spectrum disorders(PAS),and assess maternal and infant outcomes.Methods We included 590 cases of pregnancy women with placenta previa complicated by PAS who underwent cesarean sections at five hospitals in Wuhan and Xianning cities between January 2018 and June 2021.These patients were divided into three groups based on delivery gesta-tional age:EPB,LPB,and term birth(TB).A multiple logistic regression model was employed to analyze the risk factors associated with EPB and LPB.Additionally,differences in early maternal and infant outcomes among these groups were examined.Results Among 590 pregnancy women with placenta previa complicated by PAS,the proportions of EPB and LPB were 9.7%and 54.4%.The use of uterine contraction inhibitors prior to cesarean section,vaginal bleeding,and previous cesarean sections history were identified as risk factors for both EPB and LPB.The proportion of severe postpartum hemorrhage was comparable between the EPB group and the LPB group;however,the incidence of neonatal asphyxia,low birth weight infants,and the rate of newborns transferred to the Neonatal Intensive Care Unit(NICU)within 24 hours after cesarean delivery were significantly higher in the EPB group compared to the LPB group.Conclusions Placenta previa complicated by PAS predominantly leads to LPB.The history of prior cesarean sections,uterine contractions,and vaginal bleeding prior to cesarean section,are sig-nificantly associated with both EPB and LPB.During the perinatal period,efforts should be made to extend gesta-tional weeks under close monitoring to minimize the incidence of premature births and thereby improve early mater-nal and infant outcomes.