Key result
Single hidden layer backpropagation artificial neural networks predicted estimated blood loss within 127.33 ml and transfusions with 71.4% sensitivity and 85.4% specificity.
Why the study?
Preoperative identification of large estimated blood loss or transfusion need can assist caregivers in planning for possible postoperative morbidity and mortality.
Do Artificial Neural Network models improve the prediction of estimated blood loss and transfusion requirements compared to regression models in patients undergoing myomectomy?
Observational (n=96)
No
Do Artificial Neural Network models improve the prediction of estimated blood loss and transfusion requirements compared to regression models in patients undergoing myomectomy?
Artificial neural networks, particularly ensemble models, can predict perioperative transfusion needs with high sensitivity in myomectomy patients, potentially aiding in preoperative planning.
May aid myomectomy planning; leaves open superiority to regression models pending validation.
This chapter explores valuating the efficacy of using artificial neural networks (ANNs) for predicting the estimated blood loss (EBL) and also transfusion requirements of myomectomy patients. All 146 myomectomy surgeries performed over a 6-year period from a single site are captured. Records were removed for various reasons, leaving 96 cases. Backpropagation and radial basis function ANN models were developed to predict EBL and perioperative transfusion needs along with a regression model. The single hidden layer backpropagation ANN models performed the best for both prediction problems. EBL was predicted on average within 127.33 ml of measured blood loss, and transfusions were predicted with 71.4% sensitivity and 85.4% specificity. A combined ANN ensemble model using the output of the EBL ANN as an input variable to the transfusion prediction ANN was developed and resulted in 100% sensitivity and 62.9% specificity. The preoperative identification of large EBL or transfusion need can assist caregivers in better planning for possible post-operative morbidity and mortality.
No takes yet. Share an insight, caveat, or question.
Walczak et al. (2020) conducted an observational in Myomectomy (n=96). Artificial neural networks (ANNs) vs. Regression model was evaluated on Estimated blood loss (EBL) and perioperative transfusion needs. Single hidden layer backpropagation artificial neural networks predicted estimated blood loss within 127.33 ml and transfusions with 71.4% sensitivity and 85.4% specificity.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: