关键词:

先天性心脏病, 光学相干断层扫描血管成像, 围术期转归, 人工智能

Abstract:

Objective To explore the feasibility of developing an intelligent algorithm for predicting the perioperative outcomes in patients with congenital heart disease（CHD）based on optical coherence tomography （OCTA），and to provide a non⁃invasive and convenient tool for perioperative assessment and outcome prediction of CHD. Methods We prospectively collected the clinical data and preoperative OCTA images of patients undergoing congenital cardiac surgery in our hospital during the period of May 2017 to May 2021. OCTA images were labeled according to whether there was excessive postoperative bleeding or adverse perioperative composite outcomes. Data augmentation was used for the OCTA images with prognostic annotations. A deep learning outcome prediction model was trained，and then a test set was used to judge the performance of the model. Results Of 202 CHD patients， 45（22.3%）developed excessive postoperative bleeding and 58（28.7%）underwent adverse perioperative composite outcomes. In the test set，the area under the receiver operating characteristic curve（AUC）of the model for predict⁃ ing postoperative excessive bleeding was 0.82，the sensitivity and specificity were 0.90 and 0.75 respectively，and the accuracy was 0.78；the AUC of the adverse perioperative composite outcome prediction model was 0.81，the sensitivity and specificity were 0.83 and 0.80 respectively，and the accuracy was 0.81. Conclusions Preoperative OCTA images combined with artificial intelligence can accurately predict the perioperative outcomes of CHD.

Key words:

congenital heart disease, optical coherence tomography angiography, perioperative out? come, artificial intelligence