Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks

• Published in: Oral surgery, oral medicine, oral pathology and oral radiology Vol. 138; no. 1; pp. 184 - 195
• Main Authors: Lee, Han-Sol, Yang, Su, Han, Ji-Yong, Kang, Ju-Hee, Kim, Jo-Eun, Huh, Kyung-Hoe, Yi, Won-Jin, Heo, Min-Suk, Lee, Sam-Sun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2024
• Subjects: Humans; Neural Networks, Computer; Radicular Cyst - diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted - methods; Radiography, Panoramic
• ISSN: 2212-4403; 2212-4411; 2212-4411
• DOI: 10.1016/j.oooo.2023.09.012

The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs’ with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.