Imaging Science in Dentistry

Korean Academy of Oral and Maxillofacial Radiology (대한영상치의학회)
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Evaluation of maxillary sinusitis from panoramic radiographs and cone-beam computed tomographic images using a convolutional neural network

  • Serindere, Gozde (Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hatay Mustafa Kemal University) ;
  • Bilgili, Ersen (Department of Dentomaxillofacial Radiology, Izmir Education Dental Hospital) ;
  • Yesil, Cagri (Department of Computer Engineering, Faculty of Engineering, Yeditepe University) ;
  • Ozveren, Neslihan (Department of Pediatric Dentistry, Faculty of Dentistry, Trakya University)
  • Received : 2021.10.21
  • Accepted : 2022.02.23
  • Published : 2022.06.30
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Abstract

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

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References

  1. Radojicic C. Sinusitis: allergies, antibiotics, aspirin, asthma. Cleve Clin J Med 2006; 73: 671-8. https://doi.org/10.3949/ccjm.73.7.671
  2. Drettner B. Pathophysiology of paranasal sinuses with clinical implications. Clin Otolaryngol Allied Sci 1980; 5: 277-84. https://doi.org/10.1111/j.1365-2273.1980.tb01658.x
  3. Aust R, Falck B, Svanholm H. Studies of the gas exchange and pressure in the maxillary sinuses in normal and infected humans. Rhinology 1979; 17: 245-51.
  4. Mustafa M, Patawari P, Iftikhar HM, Shimmi SC, Hussain SS, Sien MM. Acute and chronic rhinosinusitis, pathophysiology and treatment. Int J Pharm Sci Invent 2015; 4: 30-6.
  5. Demuri GP, Ellen R Wald. Sinusitis. In Mendell GL, Bennett JE, Dolin R. Mandell, Douglas and Bennett's principles and practice of infectious diseases. 7th ed. Philadelphia: Churchill Livingstone/Elsevier; 2009. p. 839-49.
  6. Maillet M, Bowles WR, McClanahan SL, John MT, Ahmad M. Cone-beam computed tomography evaluation of maxillary sinusitis. J Endod 2011; 37: 753-7. https://doi.org/10.1016/j.joen.2011.02.032
  7. Obayashi N, Ariji Y, Goto M, Izumi M, Naitoh M, Kurita K, et al. Spread of odontogenic infection originating in the maxillary teeth: computerized tomographic assessment. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2004; 98: 223-31. https://doi.org/10.1016/j.tripleo.2004.05.014
  8. Anzai Y, Yueh B. Imaging evaluation of sinusitis: diagnostic performance and impact on health outcome. Neuroimaging Clin N Am 2003; 13: 251-63. https://doi.org/10.1016/S1052-5149(03)00017-0
  9. Mafee MF, Tran BH, Chapa AR. Imaging of rhinosinusitis and its complications: plain film, CT, and MRI. Clin Rev Allergy Immunol 2006; 30: 165-86. https://doi.org/10.1385/CRIAI:30:3:165
  10. Cha JY, Mah J, Sinclair P. Incidental findings in the maxillofacial area with 3-dimensional cone-beam imaging. Am J Orthod Dentofacial Orthop 2007; 132: 7-14. https://doi.org/10.1016/j.ajodo.2005.08.041
  11. White SC. Cone-beam imaging in dentistry. Health Phys 2008; 95: 628-37. https://doi.org/10.1097/01.HP.0000326340.81581.1a
  12. Ribeiro A, Keat R, Khalid S, Ariyaratnam S, Makwana M, do Pranto M, et al. Prevalence of calcifications in soft tissues visible on a dental pantomogram: a retrospective analysis. J Stomatol Oral Maxillofac Surg 2018; 119: 369-74. https://doi.org/10.1016/j.jormas.2018.04.014
  13. Resnick CM, Dentino KM, Garza R, Padwa BL. A management strategy for idiopathic bone cavities of the jaws. J Oral Maxillofac Surg 2016; 74: 1153-8. https://doi.org/10.1016/j.joms.2015.12.014
  14. Price JB, Thaw KL, Tyndall DA, Ludlow JB, Padilla RJ. Incidental findings from cone beam computed tomography of the maxillofacial region: a descriptive retrospective study. Clin Oral Implants Res 2012; 23: 1261-8. https://doi.org/10.1111/j.1600-0501.2011.02299.x
  15. Rosenfeld RM, Andes D, Bhattacharyya N, Cheung D, Eisenberg S, Ganiats TG, et al. Clinical practice guideline: adult sinusitis. Otolaryngol Head Neck Surg 2007; 137(3 Suppl): S1-31. https://doi.org/10.1016/j.otohns.2007.06.726
  16. Kuwana R, Ariji Y, Fukuda M, Kise Y, Nozawa M, Kuwada C, et al. Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomaxillofac Radiol 2021; 50: 20200171.
  17. Khanagar SB, Al-Ehaideb A, Maganur PC, Vishwanathaiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry - a systematic review. J Dent Sci 2021; 16: 508-22. https://doi.org/10.1016/j.jds.2020.06.019
  18. Wang W, Yang Y, Wang X, Wang W, Li J. Development of convolutional neural network and its application in image classification: a survey. Opt Eng 2019; 58: 040901.
  19. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A 1982; 79: 2554-8. https://doi.org/10.1073/pnas.79.8.2554
  20. Doi K. Computer aided diagnosis in medical imaging: historical review, current status, and future potential. Comput Med Imaging Graph 2007; 31: 198-211. https://doi.org/10.1016/j.compmedimag.2007.02.002
  21. Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, et al. Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol 2019; 35: 301-7. https://doi.org/10.1007/s11282-018-0363-7
  22. Maestre-Ferrin L, Galan-Gil S, Carrillo-Garcia C, Penarrocha-Diago M. Radiographic findings in the maxillary sinus: comparisonof panoramic radiography with computed tomography. Int J Oral Maxillofac Implants 2011; 26: 341-6.
  23. Yoshiura K, Ban S, Hijiya T, Yuasa K, Miwa K, Ariji E, et al. Analysis of maxillary sinusitis using computed tomography. Dentomaxillofac Radiol 1993; 22: 86-92. https://doi.org/10.1259/dmfr.22.2.8375560
  24. Ohashi Y, Ariji Y, Katsumata A, Fujita H, Nakayama M, Fukuda M, et al. Utilization of computer-aided detection system in diagnosing unilateral maxillary sinusitis on panoramic radiographs. Dentomaxillofac Radiol 2016; 45: 20150419. https://doi.org/10.1259/dmfr.20150419
  25. Kim Y, Lee KJ, Sunwoo L, Choi D, Nam CM, Cho J, et al. Deep learning in diagnosis of maxillary sinusitis using conventional radiography. Invest Radiol 2019; 54: 7-15. https://doi.org/10.1097/RLI.0000000000000503
  26. Wittkopf ML, Beddow PA, Russell PT, Duncavage JA, Becker SS. Revisiting the interpretation of positive sinus CT findings: a radiological and symptom-based review. Otolaryngol Head Neck Surg 2009; 140: 306-11. https://doi.org/10.1016/j.otohns.2008.12.007
  27. Holbrook EH, Brown CL, Lyden ER, Leopold DA. Lack of significant correlation between rhinosinusitis symptoms and specific regions of sinus computer tomography scans. Am J Rhinol 2005; 19: 382-7. https://doi.org/10.1177/194589240501900411
  28. Gwaltney JM Jr, Phillips CD, Miller RD, Riker DK. Computed tomographic study of the common cold. N Engl J Med 1994; 330: 25-30. https://doi.org/10.1056/NEJM199401063300105