Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study.

The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.

Automatic Detection of Periapical Osteolytic Lesions on Cone-beam Computed Tomography Using Deep Convolutional Neuronal Networks.

INTRODUCTION: Cone-beam computed tomography (CBCT) is an essential diagnostic tool in oral radiology. Radiolucent periapical lesions (PALs) represent the most frequent jaw lesions. However, the description, interpretation, and documentation of radiological findings, especially incidental findings, are time-consuming and resource-intensive, requiring a high degree of expertise. To improve quality, dentists may use artificial intelligence in the form of deep learning tools. This study was conducted to develop and validate a deep convolutional neuronal network for the automated detection of osteolytic PALs in CBCT data sets. METHODS: CBCT data sets from routine clinical operations (maxilla, mandible, or both) performed from January to October 2020 were retrospectively screened and selected. A 2-step approach was used for automatic PAL detection. First, tooth localization and identification were performed using the SpatialConfiguration-Net based on heatmap regression. Second, binary segmentation of lesions was performed using a modified U-Net architecture. A total of 144 CBCT images were used to train and test the networks. The method was evaluated using the 4-fold cross-validation technique. RESULTS: The success detection rate of the tooth localization network ranged between 72.6% and 97.3%, whereas the sensitivity and specificity values of lesion detection were 97.1% and 88.0%, respectively. CONCLUSIONS: Although PALs showed variations in appearance, size, and shape in the CBCT data set and a high imbalance existed between teeth with and without PALs, the proposed fully automated method provided excellent results compared with related literature.