Cargando…

Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors

(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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chiesa-Estomba, Carlos M, Sistiaga-Suarez, Jon A, González-García, José Ángel, Larruscain, Ekhiñe, Cammaroto, Giovanni, Mayo-Yánez, Miguel, Lechien, Jerome R, Calvo-Henríquez, Christian, Altuna, Xabier, Medela, Alfonso
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712376/
https://www.ncbi.nlm.nih.gov/pubmed/33036481
http://dx.doi.org/10.3390/medsci8040042
_version_ 1783618361082511360
author Chiesa-Estomba, Carlos M
Sistiaga-Suarez, Jon A
González-García, José Ángel
Larruscain, Ekhiñe
Cammaroto, Giovanni
Mayo-Yánez, Miguel
Lechien, Jerome R
Calvo-Henríquez, Christian
Altuna, Xabier
Medela, Alfonso
author_facet Chiesa-Estomba, Carlos M
Sistiaga-Suarez, Jon A
González-García, José Ángel
Larruscain, Ekhiñe
Cammaroto, Giovanni
Mayo-Yánez, Miguel
Lechien, Jerome R
Calvo-Henríquez, Christian
Altuna, Xabier
Medela, Alfonso
author_sort Chiesa-Estomba, Carlos M
collection PubMed
description (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.
format Online
Article
Text
id pubmed-7712376
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77123762020-12-04 Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors Chiesa-Estomba, Carlos M Sistiaga-Suarez, Jon A González-García, José Ángel Larruscain, Ekhiñe Cammaroto, Giovanni Mayo-Yánez, Miguel Lechien, Jerome R Calvo-Henríquez, Christian Altuna, Xabier Medela, Alfonso Med Sci (Basel) Article (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. MDPI 2020-10-07 /pmc/articles/PMC7712376/ /pubmed/33036481 http://dx.doi.org/10.3390/medsci8040042 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chiesa-Estomba, Carlos M
Sistiaga-Suarez, Jon A
González-García, José Ángel
Larruscain, Ekhiñe
Cammaroto, Giovanni
Mayo-Yánez, Miguel
Lechien, Jerome R
Calvo-Henríquez, Christian
Altuna, Xabier
Medela, Alfonso
Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors
title Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors
title_full Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors
title_fullStr Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors
title_full_unstemmed Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors
title_short Artificial Neural Network as a Tool to Predict Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors
title_sort artificial neural network as a tool to predict facial nerve palsy in parotid gland surgery for benign tumors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712376/
https://www.ncbi.nlm.nih.gov/pubmed/33036481
http://dx.doi.org/10.3390/medsci8040042
work_keys_str_mv AT chiesaestombacarlosm artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT sistiagasuarezjona artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT gonzalezgarciajoseangel artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT larruscainekhine artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT cammarotogiovanni artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT mayoyanezmiguel artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT lechienjeromer artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT calvohenriquezchristian artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT altunaxabier artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors
AT medelaalfonso artificialneuralnetworkasatooltopredictfacialnervepalsyinparotidglandsurgeryforbenigntumors