In International journal of computerized dentistry
OBJECTIVE : To develop a Deep Learning (DL) Artificial Intelligence (AI) model for the instance segmentation and numbering of teeth on Orthopantomograms (OPGs).
METHODS : Forty OPGs were manually annotated to lay down the ground truth for training two Convolutional Neural Networks (CNNs), U-net, and Faster RCNN. These algorithms were concurrently trained and validated on a dataset of 1,280 teeth (40 OPGs) each. The U-net algorithm was trained on OPGs specifically annotated with fluid margins to label all 32 teeth via instance segmentation allowing each tooth to be denoted as a separate entity from the surrounding structures. Simultaneously, teeth were also numbered as per the Fédération Dentaire Internationale (FDI) numbering system, using bounding boxes to train Faster RCNN. Consequently, both trained CNNs were combined to develop an AI model capable of segmenting and numbering all teeth on an OPG.
RESULTS : The performance of the U-net algorithm was determined using various performance metrics including precision=88.8%, accuracy=88.2%, re-call=87.3%, F-1 score=88%, dice index=92.3% and Intersection over Union (IoU)=86.3%. The performance metrics of the Faster RCNN algorithm were determined using overlap accuracy=30.2 bounding boxes (out of a possible of 32 boxes) and classifier accuracy of labels=93.8%.
CONCLUSIONS : The instance segmentation and teeth numbering results of our trained AI model were close to the ground truth, holding a promising future for its incorporation into clinical dental practice. The ability of an AI model to automatically identify teeth on OPGs will aid dentists with diagnosis and treatment planning thus increasing efficiency.
Adnan Niha, Khalid Waleed Bin, Umer Fahad
2023-Jan-27
Artificial Intelligence, Convolutional Neural Network, Deep Learning, Dentistry, Intraoral Radiography, Neural Networks
