{Reference Type}: Journal Article
{Title}: Automatic Classification and Segmentation of Multiclass Jaw Lesions in Cone-Beam Computed Tomography using Deep Learning.
{Author}: Liu W;Li X;Liu C;Gao G;Xiong Y;Zhu T;Zeng W;Guo J;Tang W;
{Journal}: Dentomaxillofac Radiol
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jun 27
{Factor}: 3.525
{DOI}: 10.1093/dmfr/twae028
{Abstract}: <B>OBJECTIVE: </B>To develop and validate a modified deep learning (DL) model based on nnU-net for classifying and segmenting five-class jaw lesions using cone-beam computed tomography (CBCT).<BR><B>METHODS: </B>A total of 368 CBCT scans (37 168 slices) were used to train a multi-class segmentation model. The data underwent manual annotation by two oral and maxillofacial surgeons (OMSs) to serve as ground truth. Sensitivity, specificity, precision, F1-score, and accuracy were used to evaluate the classification ability of the model and doctors, with or without artificial intelligence assistance. The dice similarity coefficient (DSC), average symmetric surface distance (ASSD) and segmentation time were used to evaluate the segmentation effect of the model.<BR><B>RESULTS: </B>The model achieved the dual task of classifying and segmenting jaw lesions in CBCT. For classification, the sensitivity, specificity, precision, and accuracy of the model were 0.871, 0.974, 0.874 and 0.891, respectively, surpassing oral and maxillofacial radiologists (OMFRs) and OMSs, approaching the specialist. With the model's assistance, the classification performance of OMFRs and OMSs improved, particularly for odontogenic keratocyst (OKC) and ameloblastoma (AM), with F1-score improvements ranging from 6.2% to 12.7%. For segmentation, the DSC was 87.2% and the ASSD was 1.359 mm. The model's average segmentation time was 40 ± 9.9 s, contrasting with 25 ± 7.2 min for OMSs.<BR><B>CONCLUSIONS: </B>The proposed DL model accurately and efficiently classified and segmented five classes of jaw lesions using CBCT. In addition, it could assist doctors in improving classification accuracy and segmentation efficiency, particularly in distinguishing confusing lesions (e.g., AM and OKC).