Detection of typical Pathologies and Differential Treatments in Dental Panoramic X-Rays based on Deep Convolutional Neural Network

Artificial Intelligence (AI) is gaining traction in medical imaging. There are a plethora of potential applications, covering every stage of the medical imaging life cycle from image creation to diagnosis to outcome prognosis. The use of Artificial Intelligence (AI) and Machine Learning is having a rising influence in the field of dentistry and is improving the growth of digital tools and technology, with a wide range of applications in cosmetic dental treatments and treatment planning. Deep learning has been able to match and improve human performance in fields such as image processing for extremely complex tasks such as object detection models with classification and prediction capabilities, as well as the identification of numerous dental diseases and their differential treatments. The lack of sufficiently large, curated, and representative training data with expert labeling (eg, annotations) is one of the main barriers to the development and clinical application of AI algorithms. This pilot study used a dataset of 664 dental panoramic X-ray images to build a model that can detect dental diseases and the differential treatments present on a full dental X-ray. After being manually labeled for 9 different classes, these images were used for training. The deep neural network used in this study is You Only Look Once (YOLO) version 6, a one-step search method primarily used for real-time object detection. Three classes of dental diseases and six classes of differential treatments were identified with the aid of bounding boxes. Results show that this strategy was able to attain a mean average precision (mAP) of 70.76% by training with this smaller number of images, which shows tremendous potential for dentists and radiologists.