Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation

Jiuling Weng; Shujin Xue; Xingmei Wei; Simeng Lu; Jin Xie; Ying Kong; Mengya Shen; Biao Chen; Jingyuan Chen; Xinyue Zou; Xinyi Zhang; Zhencheng Gao; Ping Liu; Ying Shi; Danmo Cui; Yongxin Li; Haihui Wang

doi:10.1007/s00405-024-08463-w

Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation

Eur Arch Otorhinolaryngol. 2024 Feb 14. doi: 10.1007/s00405-024-08463-w. Online ahead of print.

Authors

Jiuling Weng^#¹, Shujin Xue^#², Xingmei Wei², Simeng Lu², Jin Xie¹, Ying Kong², Mengya Shen², Biao Chen², Jingyuan Chen², Xinyue Zou², Xinyi Zhang², Zhencheng Gao², Ping Liu², Ying Shi², Danmo Cui², Yongxin Li², Haihui Wang³

Affiliations

¹ Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China.
² Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
³ Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China. whhmath@buaa.edu.cn.

^# Contributed equally.

PMID: 38353769
DOI: 10.1007/s00405-024-08463-w

Abstract

Objective: The objectives of this study are twofold: first, to visualize the structure of malformed cochleae through image reconstruction; and second, to develop a predictive model for postoperative outcomes of cochlear implantation (CI) in patients diagnosed with cochlear hypoplasia (CH) and incomplete partition (IP) malformation.

Methods: The clinical data from patients diagnosed with cochlear hypoplasia (CH) and incomplete partition (IP) malformation who underwent cochlear implantation (CI) at Beijing Tongren Hospital between January 2016 and August 2020 were collected. Radiological features were analyzed through 3D segmentation of the cochlea. Postoperative auditory speech rehabilitation outcomes were evaluated using the Categories of Auditory Performance (CAP) and the Speech Intelligibility Rating (SIR). This study aimed to investigate the relationship between cochlear parameters and postoperative outcomes. Additionally, a predictive model for postoperative outcomes was developed using the K-nearest neighbors (KNN) algorithm.

Results: In our study, we conducted feature selection by using patients' imaging and audiological attributes. This process involved methods such as the removal of missing values, correlation analysis, and chi-square tests. The findings indicated that two specific features, cochlear volume (V) and cochlear canal length (CDL), significantly contributed to predicting the outcomes of hearing and speech rehabilitation for patients with inner ear malformations. In terms of hearing rehabilitation, the KNN classification achieved an accuracy of 93.3%. Likewise, for speech rehabilitation, the KNN classification demonstrated an accuracy of 86.7%.

Conclusion: The measurements obtained from the 3D reconstruction model hold significant clinical relevance. Despite the considerable variability in cochlear morphology across individuals, radiological features remain effective in predicting cochlear implantation (CI) prognosis for patients with inner ear malformations. The utilization of 3D segmentation techniques and the developed predictive model can assist surgeons in conducting preoperative cochlear structural measurements for patients with inner ear malformations. This, in turn, can offer a more informed perspective on the anticipated outcomes of cochlear implantation.

Keywords: Cochlear implantation; Inner ear malformation; K-nearest neighbors; Machine learning.

Abstract

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