Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging

Article type: Research Article

Authors: Su, Hsin-Yueh^a | Hsieh, Shang-Ting^b | Tsai, Kun-Zhe^c | Wang, Yu-Li^a | Wang, Chi-Yuan^d | Hsu, Shih-Yen^e | Liu, Kuo-Ying^f | Huang, Yung-Hui^d | Wei, Ya-Wen^g | Lu, Nan-Han^{d; f; *} | Chen, Tai-Been^{d; h; *}

Affiliations: [a] Department of Radiology, Hualien Armed Forces General Hospital, Hualien County, Taiwan | [b] Department of Health Beauty, Fooyin University, Kaohsiung City, Taiwan | [c] Department of Periodontology, Mackay Memorial Hospital, Taipei City, Taiwan | [d] Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City, Taiwan | [e] Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan | [f] Department of Radiology, E-DA Cancer Hospital, I-Shou University, Kaohsiung City, Taiwan | [g] Department of Dentistry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan | [h] Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Correspondence: [*] Corresponding authors: Tai-Been Chen, Department of Medical Imaging and Radiological Science, I-Shou University, 8 Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan. E-mail: ztbchen@outlook.com; and Nan-Han Lu, Department of Radiology, E-DA Cancer Hospital, I-Shou University, 21 Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan. E-mail: ed103911@edah.org.tw.

Abstract: BACKGROUND:Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses. OBJECTIVE:This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging. METHODS AND MATERIALS:This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy. RESULTS:Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors. CONCLUSIONS:This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.

Keywords: Dental panoramic imaging, multiple positioning error, fused image features

DOI: 10.3233/XST-230171

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 6, pp. 1315-1332, 2023