# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
###### Abstract  
Dentists could fail to notice periapical lesions (PLs) while examining panoramic radiographs. Accordingly, this study aimed to develop an artificial intelligence (AI) designed to address this problem. Materials and methods: a total of 18618 periapical root areas (PRA) on 713 panoramic radiographs were annotated and classified as having or not having PLs. An AI model consisting of two convolutional neural networks (CNNs), a detector and a classifier, was trained on the images. The detector localized PRAs using a bounding-box-based object detection model, while the classifier classified the extracted PRAs as PL or not-PL using a fine-tuned CNN. The classifier was trained and validated on a balanced subset of the original dataset that included 3249 PRAs, and tested on 707 PRAs. Results: the detector achieved an average precision of 74.95%, while the classifier accuracy, sensitivity and specificity were 84%, 81% and 86%, respectively. When integrating both detection and classification models, the proposed method accuracy, sensitivity, and specificity were 84.6%, 72.2%, and 85.6%, respectively. Conclusion: a two-stage CNN model consisting of a detector and a classifier can successfully detect periapical lesions on panoramic radiographs. artificial intelligence; neural network; periapical lesion; panoramic radiographs +
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could be inconsistency across dentists in their interpretation of such radiographs, and due to radiation concerns, they cannot be routinely used for screening the entire dentition. The radiographic appearance of endodontic pathosis in a periapical radiographs could be subjective and they have shown limited discriminatory ability when compared against histopathological analysis [6; 9; 10]. A previous study showed that interpreters were only able to reach a 50% level of agreement on the assessment periapical lesions on periapical radiographs. In addition, re-evaluation of radiographs by the same clinician showed different interpretation of their own original diagnosis [10]. Thus, image interpretation by dentists could sometimes be inconsistent [11]. Other techniques such as CBCT, MRI and echography can also be useful [8; 12]. However, these methods cannot be used for routine screening because CBCT requires too much radiation, MRI is very expensive and time consuming, and echography is ineffective in lesions not affecting the cortical bone. In this context, OPG are better suited for screening. However, even though dentists are supposed to make accurate screenings of periapical lesions on OPGs, human errors occur and dentists can often miss obvious periapical lesions. A tool to automate detection can help minimize these errors. Panoramic radiographs are routinely used in dental practice because despite having lower resolution, they are able to capture an extensive area of the oral cavity with significantly lower doses of radiation compared to CBCT imaging [13; 14]. They allow the easy examination of the complete dentition including the alveolar bone, temporomandibular joints, and adjacent structures providing a valuable screening opportunity [15; 16]. Regarding the diagnosis of periapical lesions on panoramic radiographs, experienced clinicians can achieve high specificity (95.8%) but a low sensitivity (34.2%) compared to the use of CBCT [17]. Moreover, there is considerable variability in the dental professionals' abilities to read panoramic radiographs, which is affected by their individual skills, experience, and biases [18; 19]. These limitations in the assessment of panoramic radiographs may lead to misdiagnosis or mistreatment [18; 20]. Recent years have seen an increased use of artificial intelligence in all branches of medicine and dentistry. These computer programs can take over human tasks imitating intelligent human behavior, performing complex activities such as decision-making, solving problems, and even recognizing objects and words [21; 22].