Laboratory of Haihui Data Analysis, School of Mathematical Sciences, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China.
Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Eur Arch Otorhinolaryngol. 2024 Jul;281(7):3535-3545. doi: 10.1007/s00405-024-08463-w. Epub 2024 Feb 14.
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.
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.
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%.
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.
本研究旨在实现两个目标：首先，通过图像重建可视化畸形耳蜗的结构；其次，为诊断为耳蜗发育不全（CH）和不完全分隔（IP）畸形的患者的耳蜗植入（CI）术后结果开发预测模型。
收集 2016 年 1 月至 2020 年 8 月期间在北京同仁医院接受耳蜗植入（CI）的耳蜗发育不全（CH）和不完全分隔（IP）畸形患者的临床资料。通过耳蜗的 3D 分割分析影像学特征。使用听觉绩效分类（CAP）和言语可懂度评分（SIR）评估术后听觉言语康复结果。本研究旨在探讨耳蜗参数与术后结果的关系。此外，还使用 K-最近邻（KNN）算法开发了术后结果预测模型。
在本研究中，我们通过使用患者的影像学和听力学属性进行特征选择。这一过程涉及缺失值处理、相关性分析和卡方检验等方法。结果表明，两个特定特征，即耳蜗容积（V）和耳蜗管长度（CDL），对预测内耳畸形患者听力和言语康复结果具有重要意义。在听力康复方面，KNN 分类的准确率为 93.3%。同样，在言语康复方面，KNN 分类的准确率为 86.7%。
3D 重建模型获得的测量值具有重要的临床意义。尽管个体间耳蜗形态存在较大差异，但影像学特征仍然可以有效预测内耳畸形患者的耳蜗植入（CI）预后。3D 分割技术的应用和开发的预测模型可以帮助外科医生对患有内耳畸形的患者进行术前耳蜗结构测量。这反过来可以为耳蜗植入的预期结果提供更有见地的视角。
