Abstract
It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.
Highlights
Dental implants have been used for more than half a century and currently are a highly reliable treatment option for long-term replacement (10+ years) of missing teeth [1,2]
Each dental implant image included was manually cropped as needed for each dental panoramic radiograph taken
ResNet is a network that can be deepened to very deep layers of over 100 layers. This representative of the ResNet architecture has layers 18, 34, 50, 101 and 152, which were selected as the convolutional neural network (CNN) model in this study
Summary
Dental implants have been used for more than half a century and currently are a highly reliable treatment option for long-term replacement (10+ years) of missing teeth [1,2]. It is very important to be able to independently and accurately identify the type of implant used in a patient. Deep learning using CNNs are very useful for classification and diagnosis when using medical images [10]. Research on deep learning models using panoramic radiographs has been reported, including tooth detection and numbering [11], osteoporosis prescreening [12], cystic lesion detection [13,14], atherosclerotic carotid plaque detection [15], and maxillary sinusitis diagnosis [16]. The purpose of this study was to build and evaluate a method that classifies implant brands and treatment stages from dental panoramic radiographic images using a multi-task deep learning approach
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