Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images.

OBJECTIVES: The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in Cone Beam Computed Tomography (CBCT) volumes and to evaluate the performance of this model.

METHODS: In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into three parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, Area Under Curve (AUC), Dice Coefficient (DC), 95% Hausdorff Distance (95% HD), and Intersection over Union (IoU) values.

RESULTS: F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96 respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively.

CONCLUSIONS: Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.