关键词: 皮瓣移植, 静脉危象, 深度学习, 医学图像, 模型, 护理

Abstract:

Objective To construct a model for identifying venous crisis following skin flap transplantation and validate its efficacy，thereby providing objective evidence for the early and accurate recognition of venous crisis in skin flaps.Methods A prospective cohort study was conducted on patients who underwent flap transplantation at the microsurgery department of a tertiary-level hospital in Hunan Province from January 2022 to December 2024. Time-series images of the flaps during hospitalization were collected using Huawei M6 tablets to form the dataset. The Make Sense image processing software was employed to annotate regions of interest within the flaps and document their vascular status. Identification models for venous crisis after flap transplantation were established using recurrent neural networks，gated recurrent units，and long short-term memory networks，followed by internal validation. Model performance was evaluated using accuracy，precision，recall，F1 score，area under the receiver operating characteristic curve，Kolmogorov-Smirnov value for discrimination，calibration curve，and decision curve. The optimal model was selected.Results A total of 38，850 flap images from 661 patients were ultimately included. The accuracy rates of the recurrent neural network，gated recurrent unit，and long short-term memory network models were 0.948，0.949，and 0.984，respectively. Their precision rates were 0.603，0.449，and 0.776，while their recall rates were 0.711，0.700，and 0.922，respectively. Recall rates were 0.711，0.700，and 0.922，respectively. F1 scores were 0.607，0.529，and 0.811，respectively. The area under the receiver operating characteristic curve was 0.946，0.987，and 0.911，respectively，with discrimination rates of 0.885，0.963，and 0.965.Conclusion The flap venous crisis identification model constructed using flap time-series images and long short-term memory network method demonstrates excellent performance. It provides objective assessment criteria for nurses in monitoring flap perfusion，thereby facilitating early identification and intervention of venous crisis.

Key words: Flap Transplantation, Venous Crisis, Deep Learning, Medical Image, Predictive Model, Nursing Care