A novel radiological software prototype for automatically detecting the inner ear and classifying normal from malformed anatomy

Abstract

Background: To develop an effective radiological software prototype that could read Digital Imaging and Communicationsin Medicine (DICOM) files, crop the inner ear automatically based on head computed tomography(CT), and classify normal and inner ear malformation (IEM).Methods: A retrospective analysis was conducted on 2053 patients from 3 hospitals. We extracted 1200 inner earCTs for importing, cropping, and training, testing, and validating an artificial intelligence (AI) model. Automatedcropping algorithms based on CTs were developed to precisely isolate the inner ear volume. Additionally, asimple graphical user interface (GUI) was implemented for user interaction. Using cropped CTs as input, a deeplearning convolutional neural network (DL CNN) with 5-fold cross-validation was used to classify inner earanatomy as normal or abnormal. Five specific IEM types (cochlear hypoplasia, ossification, incomplete partitiontypes I and III, and common cavity) were included, with data equally distributed between classes. Both thecropping tool and the AI model were extensively validated.Results: The newly developed DICOM viewer/software successfully achieved its objectives: reading CT files,automatically cropping inner ear volumes, and classifying them as normal or malformed. The cropping tooldemonstrated an average accuracy of 92.25%. The DL CNN model achieved an area under the curve (AUC) of0.86 (95% confidence interval: 0.81-0.91). Performance metrics for the AI model were: accuracy (0.812), precision(0.791), recall (0.8), and F1-score (0.766).Conclusion: This study successfully developed and validated a fully automated workflow for classifying normalversus abnormal inner ear anatomy using a combination of advanced image processing and deep learningtechniques. The tool exhibited good diagnostic accuracy, suggesting its potential application in risk stratification.However, it is crucial to emphasize the need for supervision by qualified medical professionals when utilizing thistool for clinical decision-making.