PMID- 38353769 OWN - NLM STAT- Publisher LR - 20240214 IS - 1434-4726 (Electronic) IS - 0937-4477 (Linking) DP - 2024 Feb 14 TI - Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation. LID - 10.1007/s00405-024-08463-w [doi] AB - 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. CI - (c) 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. FAU - Weng, Jiuling AU - Weng J AD - Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China. FAU - Xue, Shujin AU - Xue S AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Wei, Xingmei AU - Wei X AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Lu, Simeng AU - Lu S AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Xie, Jin AU - Xie J AD - Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China. FAU - Kong, Ying AU - Kong Y AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Shen, Mengya AU - Shen M AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Chen, Biao AU - Chen B AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Chen, Jingyuan AU - Chen J AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Zou, Xinyue AU - Zou X AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Zhang, Xinyi AU - Zhang X AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Gao, Zhencheng AU - Gao Z AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Liu, Ping AU - Liu P AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Shi, Ying AU - Shi Y AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Cui, Danmo AU - Cui D AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Li, Yongxin AU - Li Y AD - Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China. FAU - Wang, Haihui AU - Wang H AUID- ORCID: 0000-0001-6345-0349 AD - Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China. whhmath@buaa.edu.cn. LA - eng GR - 7212015/Natural Science Foundation of Beijing Municipality/ GR - 81670923/National Natural Science Foundation of China/ PT - Journal Article DEP - 20240214 PL - Germany TA - Eur Arch Otorhinolaryngol JT - European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery JID - 9002937 SB - IM OTO - NOTNLM OT - Cochlear implantation OT - Inner ear malformation OT - K-nearest neighbors OT - Machine learning EDAT- 2024/02/14 12:43 MHDA- 2024/02/14 12:43 CRDT- 2024/02/14 11:07 PHST- 2023/09/08 00:00 [received] PHST- 2024/01/08 00:00 [accepted] PHST- 2024/02/14 12:43 [medline] PHST- 2024/02/14 12:43 [pubmed] PHST- 2024/02/14 11:07 [entrez] AID - 10.1007/s00405-024-08463-w [pii] AID - 10.1007/s00405-024-08463-w [doi] PST - aheadofprint SO - Eur Arch Otorhinolaryngol. 2024 Feb 14. doi: 10.1007/s00405-024-08463-w.