Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors.
Carlos-Miguel Chiesa-EstombaJon A Sistiaga-SuarezJosé Ángel González-GarcíaEkhiñe LarruscainGiovanni CammarotoMiguel Mayo YanezJerome R LechienChristian Calvo-HenríquezXabier AltunaAlfonso MedelaPublished in: Medical sciences (Basel, Switzerland) (2020)
(1) Background: Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery or the improvement in the preoperative radiological assessment, facial nerve injury (FNI) continues to be the most feared complication; (2) Methods: patients who underwent parotid gland surgery for benign tumors between June 2010 and June 2019 were included in this study aiming to make a proof of concept about the reliability of an artificial neural networks (AAN) algorithm for prediction of FNI and compared with a multivariate linear regression (MLR); (3) Results: Concerning prediction accuracy and performance, the ANN achieved the highest sensitivity (86.53% vs 46.23%), specificity (95.67% vs 92.59%), PPV (87.28% vs 66.94%), NPV (95.68% vs 83.37%), ROC-AUC (0.960 vs 0.769) and accuracy (93.42 vs 80.42) than MLR; and (4) Conclusions: ANN prediction models can be useful for otolaryngologists-head and neck surgeons-and patients to provide evidence-based predictions about the risk of FNI. As an advantage, the possibility to develop a calculator using clinical, radiological and histological or cytological information can improve our ability to generate patients counselling before surgery.
Keyphrases
- neural network
- end stage renal disease
- minimally invasive
- newly diagnosed
- chronic kidney disease
- ejection fraction
- coronary artery bypass
- healthcare
- patients undergoing
- machine learning
- deep learning
- percutaneous coronary intervention
- acute coronary syndrome
- hiv infected
- soft tissue
- health information
- patient reported