Using Artificial Intelligence to identify canine impaction in dental panoramic radiographs
Creators: name: Taylor, Daniel
Publisher: name: University of Iowa
Year: 2023
Subjects: public abstract

description: Introduction
description: In the field of dentistry, many developmental anomalies can be treated most effectively when identified and addressed early. When anomalies are discovered and treated early, treatment can be both less invasive as well as less time consuming for patients. Common among the type of anomalies just described are impacted maxillary canines. However, despite the ideal circumstances that would address this type of condition early, patients with impacted maxillary canines often first report to the orthodontist later than would be optimal. Consequences of this delayed diagnosis and treatment are frequently a more invasive approach, sometimes with uncertain prognosis for the affected teeth. Therefore, the dental profession, as well as the patient population to whom they serve, would greatly benefit from an automated way to identify and predict potential maxillary canine impactions. While our goal is to eventually predict maxillary canine impactions early (during primary or early mixed dentition), we first must achieve accurate automated identification of what an impacted maxillary canine is based on a dental panoramic radiograph.
description: Many fields within healthcare have successfully utilized the burgeoning technology of artificial intelligence. Artificial intelligence has proved invaluable to healthcare providers for the identification of trends or risk factors in their patients that would go unnoticed by the clinician. In the circumstance of potential maxillary canine impaction, the incorporation of artificial intelligence with dental records may successfully identify those at risk early enough to prevent future negative sequalae commonly associated with impacted maxillary canines. This study seeks to continue and to improve upon a previous study which utilized artificial intelligence on a subset of cases to identify and predict maxillary canine impaction based on dental panoramic radiographs.
description: Materials and Methods
description: Our study used a similar structure to the previous study performed by Welk et al. in 2021 as we utilized two separate image sets to train, validate, and test the chosen algorithm. One set of images was specific for impaction cases and a separate set of images for control cases. Our goal to improve upon the previous study included eliminating duplicate images and eliminating case and control images of questionable and poor quality. We also verified the maxillary canine diagnosis illustrated in each image and incorporated a larger impaction case sample size that would match our sample size of control images. A large difference between our study and the previous study was the incorporation of the image-recognition capabilities of ProtoPNet with the convolutional layers of DenseNet-121 and VGG-19.
description: Results
description: Convolutional Networks DenseNet-121 and VGG-19 were tested separately in conjunction with ProtoPNet to evaluate their ability to identify maxillary canine impaction based on a dental panoramic radiograph. Varying quantities of prototypes were used in the training process to refine and improve accuracy of the algorithm. Accuracy values obtained ranged from 62%-92% in correctly identifying impacted maxillary canine cases from non-impacted maxillary canine controls which is a substantial improvement from the 38-62% accuracy range obtained prior.
description: Conclusions
description: We utilized two different pre-trained, open-source convolutional neural networks in conjunction with the image-recognition model ProtoPNet to identify impacted maxillary canines versus normally erupting maxillary canines on dental panoramic radiographs. Using differing numbers of prototypes for identification showed an accuracy in identification that ranged from 62%-92%. This study shows that with high quality training and proper parameters, AI can be used to successfully identify impacted maxillary canines.