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

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.

Machine learning-assisted diagnosis of parotid tumor by using contrast-enhanced CT imaging features.

PURPOSE: This study aims to develop a machine learning diagnostic model for parotid gland tumors based on preoperative contrast-enhanced CT imaging features to assist in clinical decision-making. MATERIALS AND METHODS: Clinical data and contrast-enhanced CT images of 144 patients with parotid gland tumors from the Peking University School of Stomatology Hospital, collected from January 2019 to December 2022, were gathered. The 3D slicer software was utilized to accurately annotate the tumor regions, followed by exploring the correlation between multiple preoperative contrast-enhanced CT imaging features and the benign or malignant nature of the tumor, as well as the type of benign tumor. A prediction model was constructed using the k-nearest neighbors (KNN) algorithm. RESULTS: Through feature selection, four key features-morphology, adjacent structure invasion, boundary, and suspicious cervical lymph node metastasis-were identified as crucial in preoperative discrimination between benign and malignant tumors. The KNN prediction model achieved an accuracy rate of 94.44 %. Additionally, six features including arterial phase CT value, age, delayed phase CT value, pre-contrast CT value, venous phase CT value, and gender, were also significant in the classification of benign tumors, with a KNN prediction model accuracy of 95.24 %. CONCLUSION: The machine learning model based on preoperative contrast-enhanced CT imaging features can effectively discriminate between benign and malignant parotid gland tumors and classify benign tumors, providing valuable reference information for clinicians.