Facial nerve palsy following parotid gland surgery:A machine learning prediction outcome approach  

作　　者：Carlos M.Chiesa‐Estomba Jose A.González‐García Ekhiñe Larruscain Jon A.Sistiaga Suarez Miquel Quer Xavier León Paula Martínez‐Ruiz de Apodaca Celia López‐Mollá Miguel Mayo‐Yanez Alfonso Medela 

机构地区：[1]Department of Otorhinolaryngology—Headand Neck Surgery,Donostia University Hospital,Donosti‐San Sebastián,Spain [2]Head&Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies(YO‐IFOS),Paris,France [3]Biodonostia Health Research Institute,San Sebastián,Spain [4]Department of Otorhinolaryngology,Hospital Santa Creu I Sant Pau,UniversitatAutònoma de Barcelona,Barcelona,Spain [5]Department of Otorhinolaryngology,Doctor Peset University Hospital,Valencia,Spain [6]Otorhinolaryngology—Head and Neck Surgery Department,Complexo Hospitalario Universitario A Coruña(CHUAC),A Coruña,Galicia,Spain [7]Clinical Research in Medicine,International Center for Doctorate and Advanced Studies(CIEDUS),Universidade de Santiago de,Compostela(USC),Santiago de Compostela,Galicia,Spain [8]LEGIT Health,Bilbao,Spain

出　　处：《World Journal of Otorhinolaryngology-Head and Neck Surgery》2023年第4期271-279,共9页世界耳鼻咽喉头颈外科杂志（英文）

摘　　要：Introduction:Machine learning (ML)‐based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now.Three distinct ML models,random forest (RF),K‐nearest neighbor,and artificial neural network (ANN),for the prediction of FNI were evaluated in this mode.Methods:A retrospective,longitudinal,multicentric study was performed,including patients who went through parotid gland surgery for benign tumors at three different university hospitals.Results:Seven hundred and thirty‐six patients were included.The most compelling aspects related to risk escalation of FNI were as follows:(1) location,in the mid‐portion of the gland,near to or above the main trunk of the facial nerve and at the top part,over the frontal or the orbital branch of the facial nerve;(2) tumor volume in the anteroposterior axis;(3) the necessity to simultaneously dissect more than one level;and (4) the requirement of an extended resection compared to a lesser extended resection.By contrast,in accordance with the ML analysis,the size of the tumor (>3 cm),as well as gender and age did not result in a determining favor in relation to the risk of FNI.Discussion:The findings of this research conclude that ML models such as RF and ANN may serve evidence‐based predictions from multicentric data regarding the risk of FNI.Conclusion:Along with the advent of ML technology,an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical,radiological,histological,and/or cytological data.

关 键 词：GLAND machine learning PAROTID personalized medicine SURGERY 

分 类 号：R782[医药卫生—口腔医学]