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Machine Learning Approach for Prediction of Hearing Preservation in Vestibular Schwannoma Surgery

Authors
 Dongchul Cha  ;  Seung Ho Shin  ;  Sung Huhn Kim  ;  Jae Young Choi  ;  In Seok Moon 
Citation
 SCIENTIFIC REPORTS, Vol.10(1) : 7136, 2020-04 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2020-04
Abstract
In vestibular schwannoma patients with functional hearing status, surgical resection while preserving the hearing is feasible. Hearing levels, tumor size, and location of the tumor have been known to be candidates of predictors. We used a machine learning approach to predict hearing outcomes in vestibular schwannoma patients who underwent hearing preservation surgery: middle cranial fossa, or retrosigmoid approach. After reviewing the medical records of 52 patients with a pathologically confirmed vestibular schwannoma, we included 50 patient's records in the study. Hearing preservation was regarded as positive if the postoperative hearing was within serviceable hearing (50/50 rule). The categorical variable included the surgical approach, and the continuous variable covered audiometric and vestibular function tests, and the largest diameter of the tumor. Four different algorithms were lined up for comparison of accuracy: support vector machine(SVM), gradient boosting machine(GBM), deep neural network(DNN), and diffuse random forest(DRF). The average accuracy of predicting hearing preservation ranged from 62% (SVM) to 90% (DNN). The current study is the first to incorporate machine learning methodology into a prediction of successful hearing preservation surgery. Although a larger population may be needed for better generalization, this study could aid the surgeon's decision to perform a hearing preservation approach for vestibular schwannoma surgery.
Files in This Item:
T202001441.pdf Download
DOI
10.1038/s41598-020-64175-1
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Otorhinolaryngology (이비인후과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sung Huhn(김성헌)
Moon, In Seok(문인석) ORCID logo https://orcid.org/0000-0002-3951-5074
Choi, Jae Young(최재영) ORCID logo https://orcid.org/0000-0001-9493-3458
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/176154
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