{Reference Type}: Journal Article
{Title}: YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
{Author}: Beser B;Reis T;Berber MN;Topaloglu E;Gungor E;Kılıc MC;Duman S;Çelik Ö;Kuran A;Bayrakdar IS;
{Journal}: BMC Med Imaging
{Volume}: 24
{Issue}: 1
{Year}: 2024 Jul 11
{Factor}: 2.795
{DOI}: 10.1186/s12880-024-01338-w
{Abstract}: <B>OBJECTIVE: </B>In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.<BR><B>METHODS: </B>A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program. The dataset was divided into three subsets: training (n = 3093, 80% of the total), validation (n = 387, 10% of the total) and test (n = 385, 10% of the total). An artificial intelligence (AI) algorithm using YOLO-v5 models were developed.<BR><B>RESULTS: </B>The sensitivity, precision, F-1 score, and mean average precision-0.5 (mAP-0.5) values were 0.99, 0.99, 0.99, and 0.98 respectively, to teeth detection. The sensitivity, precision, F-1 score, and mAP-0.5 values were 0.98, 0.98, 0.98, and 0.98, respectively, to teeth segmentation.<BR><B>CONCLUSIONS: </B>YOLO-v5 based models can have the potential to detect and enable the accurate segmentation of deciduous and permanent teeth using PRs of pediatric patients with mixed dentition.