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Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach
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....
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696266/ http://dx.doi.org/10.1002/wjo2.94 |
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author | Chiesa‐Estomba, Carlos M. González‐García, Jose A. Larruscain, Ekhiñe Sistiaga Suarez, Jon A. Quer, Miquel León, Xavier Martínez‐Ruiz de Apodaca, Paula López‐Mollá, Celia Mayo‐Yanez, Miguel Medela, Alfonso |
author_facet | Chiesa‐Estomba, Carlos M. González‐García, Jose A. Larruscain, Ekhiñe Sistiaga Suarez, Jon A. Quer, Miquel León, Xavier Martínez‐Ruiz de Apodaca, Paula López‐Mollá, Celia Mayo‐Yanez, Miguel Medela, Alfonso |
author_sort | Chiesa‐Estomba, Carlos M. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10696266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106962662023-12-06 Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach Chiesa‐Estomba, Carlos M. González‐García, Jose A. Larruscain, Ekhiñe Sistiaga Suarez, Jon A. Quer, Miquel León, Xavier Martínez‐Ruiz de Apodaca, Paula López‐Mollá, Celia Mayo‐Yanez, Miguel Medela, Alfonso World J Otorhinolaryngol Head Neck Surg Research Papers 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. John Wiley and Sons Inc. 2023-03-31 /pmc/articles/PMC10696266/ http://dx.doi.org/10.1002/wjo2.94 Text en © 2023 The Authors. World Journal of Otorhinolaryngology ‐ Head and Neck Surgery published by John Wiley & Sons Ltd on behalf of Chinese Medical Association. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Papers Chiesa‐Estomba, Carlos M. González‐García, Jose A. Larruscain, Ekhiñe Sistiaga Suarez, Jon A. Quer, Miquel León, Xavier Martínez‐Ruiz de Apodaca, Paula López‐Mollá, Celia Mayo‐Yanez, Miguel Medela, Alfonso Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach |
title | Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach |
title_full | Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach |
title_fullStr | Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach |
title_full_unstemmed | Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach |
title_short | Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach |
title_sort | facial nerve palsy following parotid gland surgery: a machine learning prediction outcome approach |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696266/ http://dx.doi.org/10.1002/wjo2.94 |
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