# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 4 Discussion  
The experimental results of this study showed the effectiveness of the proposed model to detect periapical lesions on panoramic radiographs. This model could be useful in clinical application for quick and easy detection of periapical lesions; on average, the proposed AI system takes 2.3 s to detect and classify all PRAs as H or PL on a panoramic radiograph. In the present study, we proposed a two-stage deep learning architecture for periapical lesion detection on panoramic radiographs. We employed Faster-RCNN to localize PRAs and Inception v3 to classify PRA as PL or H. Breaking the task of PL detection into two steps, detection and classification, presented a faster learning convergence and higher detection rates (AP50 74.95%) compared to the baseline model in which we trained purely on PL samples (AP50 38.5%) The Inception-v3 classifier achieved the highest performance compared to other models (VGG16, Xception). When merging both detection and classification models, the overall accuracy, sensitivity, and specificity were 84.6%, 72.2%, and 85.6%, respectively. Few studies addressed the problem of periapical lesions detection on panoramic radiographs using deep learning. [PERSON] et al. [5] proposed a classification model using a small dataset (85 panoramic radiographs). The model was not sensitive enough to be used clinically, and was limited in its ability to automate the detection of the region of interest, PRAs, as it was trained on manually cropped patches of the individual teeth. In our study, we proposed an AI architecture that can automatically detect and classify PL on an entire panoramic radiograph without the need for manual processing. Automatic evaluation of panoramic radiological images was also explored in another study that assessed the reliability of a CNN based automatic software \"Diagnocat\" [26]. Upon evaluation of thirty panoramic radiographs, their CNN based automatic protocol showed very high sensitivity with respect to dental fillings, endodontically treated teeth, residual roots, periodontal bone loss, missing teeth, and prosthetic restorations. However, the reliability obtained for caries and periapical lesions assessments was unacceptable (ICC = 0.681 and 0.619, respectively). Two other studies followed a segmentation approach to localize PL on panoramic radiographs [27, 28]. [PERSON] et al. [27] presented a deep learning-based model trained on 2902 de-identified panoramic radiographs. In order to validate the algorithm 24 oral and maxillofacial surgeons assessed the presence or appearance of periapical radiolucencies on a separate set of panoramic radiographs. The findings of this study showed that the developed model outperformed 14 out of 24 surgeons. Their model achieved a precision of 67% and sensitivity of 51% on a test set of 102 radiographs. [PERSON] et al. [28] employed a UNet model trained on 470 panoramic radiographs to segment PLs. The model was tested on a small dataset consisting of 63 PL on 47 panoramic radiographs. The sensitivity, precision, and F1-score of UNet were 0.92, 0.84, and 0.88, respectively [28]. Our study was validated in a much larger sample of 299 PL on 143 panoramic radiographs, and still we achieved high accuracy, sensitivity, and specificity (84.6%, 72.2%, and 85.6%, respectively). In addition to OPG, other radiographic modalities have also been investigated for automated diagnosis of PL. In one study, CBCT images of 153 periapical lesions were evaluated by deep CNN, which detected 142 periapical lesions, along with the location and volume of lesions [29]. Another study used using CNN on periapical radiographs showed the possibility of automatically identifying and judging periapical lesions with a success rate of as high as 92.75% [11]. Diagnosing and documenting pathologies on dental radiographs is time-consuming and even though general and specialist dentis are well trained to do this, they are not exempt of human error.