System Identification Position Error in Panoramic Radiography: a Review
DOI:
https://doi.org/10.26555/jiteki.v11i1.30598Keywords:
Position error, Panoramic radiography, Automatic, Identification, Artificial intelligenceAbstract
The professionalism of the radiologist greatly influences the results of radiological images. The quality of panoramic radiography greatly influences accurate clinical diagnosis. The correct patient position is one of the many factors that affect high-quality and accurate panoramic radiography. The process of taking radiographic images causes radiation exposure to the patient, so that when taking radiographic images repeatedly it is very bad for the patient. A review research is needed to reduce radiation exposure by improving the quality of panoramic radiography. This research conducted a literature review by proposing the questions (1) What types of position errors in panoramic radiography have been researched? (2) How is the process of identifying position errors in panoramic radiography that have been researched? The results of the review research showed that the types of position errors in panoramic radiography that have been researched are the head turning, the tongue not sticking to the palate, the chin down, the chin not resting on the support. The process of evaluating position errors in panoramic radiography is mostly done manually, there is only one research that identifies position errors in panoramic radiography automatically using SVM. That there is a great opportunity to create an automatic system for identifying position errors in panoramic radiography to be more precise and time efficient.
References
[1] N. Kumar, “Assessment of common errors and subjective quality of digital panoramic radiographs in a dental institution,” Dent. Med. Res., vol. 8, no. 1, 2020, https://doi.org/10.4103/dmr.dmr_22_19.
[2] A. S. Lingam, P. Koppolu, R. Abdulsalam, R. L. Reddy, A. Anwarullah, and D. Koppolu, “Assessment of common errors and subjective quality of digital panoramic radiographs in dental institution, Riyadh,” Ann. Afr. Med., vol. 22, no. 1, 2023, https://doi.org/10.4103/aam.aam_213_21.
[3] S. Y. Lin and H. Y. Chang, “Tooth Numbering and Condition Recognition on Dental Panoramic Radiograph Images Using CNNs,” IEEE Access, vol. 9, 2021, https://doi.org/10.1109/ACCESS.2021.3136026.
[4] R. Izzetti, M. Nisi, G. Aringhieri, L. Crocetti, F. Graziani, and C. Nardi, “Basic knowledge and new advances in panoramic radiography imaging techniques: A narrative review on what dentists and radiologists should know,” Appl. Sci., vol. 11, no. 17, 2021, https://doi.org/10.3390/app11177858.
[5] S. Singh, I. Singh, F, Ahmed, and A, Baba, “Retrospective Study: Evaluating the Positioning Errors in Digital Panoramic Radiographs,” Indian J. Contemp. Dent., vol. 10, no. 2, 2022, https://doi.org/10.37506/ijocd.v10i2.18413.
[6] E. Delamare, X. Fu, Z. Huang, and J. Kim, “Panoramic imaging errors in machine learning model development: a systematic review,” Dentomaxillofac. Radiol., vol. 53, no. 3, 2024, https://doi.org/10.1093/dmfr/twae002.
[7] R. Kamdar, V. Narayan, and M. Arun, “Common patient positioning errors in digital panoramic radiographs of patients visiting a private dental institution,” Int. J. Dent. Oral Sci., vol. 6, no. 2, 2019, https://doi.org/10.19070/2377-8075-SI02-02008.
[8] N. Ozdil, O. Oflezer, B. Gokkaya, H. Bahadir, and K. Orhan, “Improper patient positioning incidence for panoramic radiographic imaging of pediatric patients: Retrospective study,” Balk. J. Dent. Med., vol. 26, no. 2, 2022, https://doi.org/10.5937/bjdm2202088o.
[9] H. Maghbuli, T. Razi, E. Banakar, P. Emamverdizadeh, and S. Razi, “Positioning Errors in Panoramic Images Based on the Dentition Type of Patients Referring to the Oral and Maxillofacial Radiology Department of Tabriz Dental School During 2017-2018,” Avicenna J. Dent. Res., vol. 15, no. 2, 2023, https://doi.org/10.34172/ajdr.2023.535.
[10] S. Kattimani, P. Kempwade, D. N. S. V. Ramesh, A. Byatnal, S. Nasreen, and T. Subashani, “Determination of different positioning errors in digital panoramic radiography: A retrospective study,” J. Med. Radiol. Pathol. Surg., vol. 6, no. 2, 2019, https://doi.org/10.15713/ins.jmrps.159.
[11] E. Negro-Calduch, N. Azzopardi-Muscat, R. S. Krishnamurthy, and D. Novillo-Ortiz, “Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews,” Int. J. Med. Inform., vol. 152, p. 104507, 2021, https://doi.org/10.1016/j.ijmedinf.2021.104507.
[12] K. Panetta, R. Rajendran, A. Ramesh, S. Rao, and S. Agaian, “Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems,” IEEE J. Biomed. Heal. Informatics, vol. 26, no. 4, 2022, https://doi.org/10.1109/JBHI.2021.3117575.
[13] V. Geetha, K. S. Aprameya, and D. M. Hinduja, “Dental caries diagnosis in digital radiographs using back-propagation neural network,” Heal. Inf. Sci. Syst., vol. 8, no. 1, 2020, https://doi.org/10.1007/s13755-019-0096-y.
[14] M. P. Muresan, A. R. Barbura, and S. Nedevschi, “Teeth Detection and Dental Problem Classification in Panoramic X-Ray Images using Deep Learning and Image Processing Techniques,” IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 457-463, 2020, https://doi.org/10.1109/ICCP51029.2020.9266244.
[15] A. Fariza, A. Z. Arifin, E. R. Astuti, and T. Kurita, “Segmenting tooth components in dental X-ray images using Gaussian kernel- based conditional spatial Fuzzy C-Means clustering algorithm,” Int. J. Intell. Eng. Syst., 2019, https://doi.org/10.22266/IJIES2019.0630.12.
[16] R. Indraswari, A. Z. Arifin, N. Suciati, E. R. Astuti, and T. Kurita, “Automatic segmentation of mandibular cortical bone on cone-beam CT images based on histogram thresholding and polynomial fitting,” Int. J. Intell. Eng. Syst., 2019, https://doi.org/10.22266/ijies2019.0831.13.
[17] Y. Faradhilla, A. Z. Arifin, N. Suciati, E. R. Astuti, R. Indraswari, and M. Widiasri, “Residual Fully Convolutional Network for Mandibular Canal Segmentation,” Int. J. Intell. Eng. Syst., vol. 14, no. 6, 2021, https://doi.org/10.22266/ijies2021.1231.20.
[18] N. Nafiiyah, C. Fatichah, D. Herumurti, E. R. Astuti, R. H. Putra, and E. Prakasa, “Mandibular segmentation on panoramic radiographs with CNN Transfer Learning,” in IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), pp. 1–5, 2022, https://doi.org/10.1109/COMNETSAT56033.2022.9994407.
[19] N. Nafi’iyah, C. Fatichah, D. Herumurti, E. R. Astuti, and R. H. Putra, “MobileNetV2 Ensemble Segmentation for Mandibular on Panoramic Radiography,” Int. J. Intell. Eng. Syst., vol. 16, no. 2, 2023, https://doi.org/10.22266/ijies2023.0430.45.
[20] S. Yang et al., “Automatic segmentation of inferior alveolar canal with ambiguity classification in panoramic images using deep learning,” Heliyon, vol. 9, no. 2, 2023, https://doi.org/10.1016/j.heliyon.2023.e13694.
[21] D. V. Tuzoff et al., “Tooth detection and numbering in panoramic radiographs using convolutional neural networks,” Dentomaxillofacial Radiol., vol. 48, no. 4, 2019, https://doi.org/10.1259/dmfr.20180051.
[22] M. Estai et al., “Deep learning for automated detection and numbering of permanent teeth on panoramic images,” Dentomaxillofacial Radiol., vol. 51, no. 2, 2022, https://doi.org/10.1259/dmfr.20210296.
[23] R. H. Putra et al., “Automated permanent tooth detection and numbering on panoramic radiograph using a deep learning approach,” Oral Surg. Oral Med. Oral Pathol. Oral Radiol., vol. 137, no. 5, 2024, https://doi.org/10.1016/j.oooo.2023.06.003.
[24] Z. Cao et al., “DPML: Prior-guided multitask learning for dental object recognition on limited panoramic radiograph dataset,” Expert Syst. Appl., vol. 254, 2024, https://doi.org/10.1016/j.eswa.2024.124446.
[25] H. Li et al., “An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs,” Front. Physiol., vol. 12, 2021, https://doi.org/10.3389/fphys.2021.655556.
[26] N. Nafiiyah, A. I. Hanifah, E. Susanto, E. R. Astuti, R. H. Putra, and E. Setyati, “Analysis of machine learning methods for gender and age identification,” J. Theor., vol. 102, no. 18, pp. 6588–6600, 2024, https://doi.org/10.1007/978-981-97-3556-3_2.
[27] M. Aljabri et al., “Canine impaction classification from panoramic dental radiographic images using deep learning models,” Informatics Med. Unlocked, vol. 30, 2022, https://doi.org/10.1016/j.imu.2022.100918.
[28] C. Muramatsu et al., “Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data,” Oral Radiol., vol. 37, no. 1, 2021, https://doi.org/10.1007/s11282-019-00418-w.
[29] E. Choi et al., “Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography,” Sci. Rep., vol. 12, no. 1, 2022, https://doi.org/10.1038/s41598-022-06483-2.
[30] S. Yilmaz, M. Tasyurek, M. Amuk, M. Celik, and E. M. Canger, “Developing deep learning methods for classification of teeth in dental panoramic radiography,” Oral Surg. Oral Med. Oral Pathol. Oral Radiol., vol. 138, no. 1, 2024, https://doi.org/10.1016/j.oooo.2023.02.021.
[31] R. Rokhshad et al., “Deep learning for age estimation from panoramic radiographs: A systematic review and meta-analysis,” J. Dent., vol. 154, 2025, https://doi.org/10.1016/j.jdent.2025.105560.
[32] E. R. Astuti, H. B. Iskandar, H. Nasutianto, B. Pramatika, D. Saputra, and R. H. Putra, “Radiomorphometric of the Jaw for Gender Prediction: A Digital Panoramic Study,” Acta Med. Philipp., 2021, https://doi.org/10.47895/amp.vi0.3175.
[33] N. Nafi’iyah et al., “System of gender identification and age estimation from radiography: a review,” Int. J. Electr. Comput. Eng., vol. 13, no. 5, 2023, https://doi.org/10.11591/ijece.v13i5.pp5491-5500.
[34] N. Nafi’iyah, C. Fatichah, D. Herumurti, E. R. Astuti, R. H. Putra, and A. S. Akbar, “Automatic for Generating Landmark Mandibular Panoramic Radiography Image,” Int. J. Intell. Eng. Syst., vol. 17, no. 1, pp. 584–595, 2024, https://doi.org/10.22266/ijies2024.0229.49.
[35] N. Nafiiyah, C. Fatichah, D. Herumurti, E. R. Astuti, and R. H. Putra, “Multitask Learning for Gender Identification and Age Group Based on the Mandible on Panoramic Radiographs,” J. Theor. Appl. Inf. Technol., vol. 101, no. 23, pp. 7998–8007, 2023, https://scholar.unair.ac.id/en/publications/multitask-learning-for-gender-identification-and-age-group-based-.
[36] S. I. Datau et al., “Linear measurements of the mandible on panoramic radiograph for sex estimation in populations in Yogyakarta, Indonesia,” Egypt. J. Forensic Sci., vol. 14, no. 1, 2024, https://doi.org/10.1186/s41935-024-00410-4.
[37] N. Nafiiyah, A. I. Hanifah, E. Susanto, E. R. Astuti, and C. Fatichah, “Nonlinear regression analysis to predict mandibular landmarks on panoramic radiographs,” Int. J. Electr. Comput. Eng., vol. 15, no. 2, pp. 2098–2108, 2025, https://doi.org/10.11591/ijece.v15i2.pp2098-2108.
[38] N. R. Suparno, A. Faizah, and A. N. Nafisah, “Assessment of Panoramic Radiograph Errors: An Evaluation of Patient Preparation and Positioning Quality at Soelastri Dental and Oral Hospital,” Open Dent. J., vol. 17, no. 1, 2023, https://doi.org/10.2174/0118742106261974230925073155.
[39] B. Kaplan, “Evaluation of error types seen in digital panoramic radiographs,” Dicle Dental Journal, vol. 25, no. 3, pp. 73–78, 2024, https://doi.org/10.70509/dicledentj.1519676.
[40] E. D. Costa, W. G. Cral, F. P. Murad, M. L. Oliveira, G. M. B. Ambrosano, and D. Q. Freitas, “Prevalence of Errors and Number of Retakes in Panoramic Radiography: Influence of Professional Training and Patient Characteristics,” Int. J. Odontostomatol., vol. 15, no. 3, 2021, https://doi.org/10.4067/s0718-381x2021000300719.
[41] S. Asriningrum, I. Sudrajat, and M. Meliawati, “Technical Error Factors On Panoramic Radiographic Examination At The Radiology Installation Of Unjani Dental and Mouth Hospital,” Int. J. Business, Econ. Soc. Dev., vol. 4, no. 4, 2023, https://doi.org/10.46336/ijbesd.v4i4.523.
[42] H. Mohammad-Rahimi et al., “Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study,” Diagnostics, vol. 13, no. 5, 2023, https://doi.org/10.3390/diagnostics13050996.
[43] H. Y. Su et al., “Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging,” J. Xray. Sci. Technol., vol. 31, no. 6, 2023, https://doi.org/10.3233/XST-230171.
[44] R. Abdalla-Aslan, T. Yeshua, D. Kabla, I. Leichter, and C. Nadler, “An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography,” Oral Surg. Oral Med. Oral Pathol. Oral Radiol., vol. 130, no. 5, 2020, https://doi.org/10.1016/j.oooo.2020.05.012.
[45] T. Ekert et al., “Deep Learning for the Radiographic Detection of Apical Lesions,” J. Endod., vol. 45, no. 7, 2019, https://doi.org/10.1016/j.joen.2019.03.016.
[46] J. H. Lee, Y. T. Kim, J. Bin Lee, and S. N. Jeong, “Deep learning improves implant classification by dental professionals: a multi-center evaluation of accuracy and efficiency,” J. Periodontal Implant Sci., vol. 52, 2022, https://doi.org/10.5051/JPIS.2104080204.
[47] K. Warin, W. Limprasert, S. Suebnukarn, S. Inglam, P. Jantana, and S. Vicharueang, “Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs,” Int. J. Oral Maxillofac. Surg., vol. 51, no. 11, 2022, https://doi.org/10.1016/j.ijom.2022.03.056.
[48] M. Bonfanti-Gris, A. Garcia-Cañas, R. Alonso-Calvo, M. P. Salido Rodriguez-Manzaneque, and G. Pradies Ramiro, “Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs,” J. Dent., vol. 126, 2022, https://doi.org/10.1016/j.jdent.2022.104301.
[49] H. Zhu, Z. Cao, L. Lian, G. Ye, H. Gao, and J. Wu, “CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image,” Neural Comput. Appl., vol. 35, no. 22, 2023, https://doi.org/10.1007/s00521-021-06684-2.
[50] D. Yu, J. Hu, Z. Feng, M. Song, and H. Zhu, “Deep learning based diagnosis for cysts and tumors of jaw with massive healthy samples,” Sci. Rep., vol. 12, no. 1, 2022, https://doi.org/10.1038/s41598-022-05913-5.
[51] J. Liu, Y. Liu, S. Li, S. Ying, L. Zheng, and Z. Zhao, “Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs,” J. Dent., vol. 125, 2022, https://doi.org/10.1016/j.jdent.2022.104239.
[52] T. S. Tunis, I. Hershkovitz, H. May, A. D. Vardimon, R. Sarig, and N. Shpack, “Variation in chin and mandibular symphysis size and shape in males and females: A CT-based study,” Int. J. Environ. Res. Public Health, vol. 17, no. 12, 2020, https://doi.org/10.3390/ijerph17124249.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nur Nafiiyah, Eha Renwi Astuti, Ramadhan Hardani Putra , Alhidayati Asymal
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License
