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A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study

Yıl 2023, Cilt: 13 Sayı: 4, 883 - 888, 29.12.2023

Doğaçhan Mertoğlu Gaye Keser Filiz Mediha Namdar Pekiner İbrahim Şevki Bayrakdar Özer Çelik Kaan Orhan

https://doi.org/10.33808/clinexphealthsci.1219160

Öz

Objective: n this study, in order to test the usability of artificial intelligence technologies in dentistry, which are becoming widespread and expanding day by day, and to investigate ways to benefit more from artificial intelligence technologies; a tooth detection and numbering study was performed on panoramic radiographs using a deep learning software.
Methods: A radiographic dataset containing 200 anonymous panoramic radiographs collected from individuals over the age of 18 was assessed in this retrospective investigation. The images were separated into three groups: training (80%), validation (10%), and test (10%), and tooth numbering was performed with the DCNN artificial intelligence software.
Results: The D-CNN system has been successful in detecting and numbering teeth. of teeth. The predicted precision, sensitivity, and F1 score were 0.996 (98.0%), 0.980 (98.0%), and 0.988 (98.8%), respectively.
Conclusion: The precision, sensitivity and F1 scores obtained in our study were found to be high, as 0.996 (98.0%), 0.980 (98.0%) and 0.988 (98.8%), respectively. Although the current algorithm based on Faster R-CNN shows promising results, future studies should be done by increasing the number of data for better tooth detection and numbering results.

Anahtar Kelimeler

Artificial intelligence Deep learning Panoramic radiography Tooth numbering

Kaynakça

  • Shah N, Bansal N, Logani A. Recent advances in imaging technologies in dentistry. World J Radiol. 2014;6(10):794-807. DOI:10.4329/wjr.v6.i10.794.
  • Choi JW. Assessment of panoramic radiography as a national oral examination tool: Review of the literature. Imaging Sci Dent. 2011; 41:1–6. DOI: 10.5624/isd.2011.41.1.1.
  • Bilgir E, Bayrakdar IS, Celik O, Orhan K, Akkoca F, Saglam H, Odabas A, Aslan AF, Ozcetin C, Kıllı M, Rozylo-Kalinowska I. An artifıcial intelligence approach to automatic tooth detection and numbering in panoramic radiographs. BMC Med Imaging. 2021; 21:124. DOI: 10.1186/s12880-021-00656-7.
  • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020; 99:769-774. DOI: 10.1177/0022034520915714.
  • Kim J, Lee HS, Song IS, Jung KH. DeNTNet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. 2019; 9:17615. DOI: 10.1038/s41598-019-53758-2.
  • Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol. 2020; 49:20190107. DOI: 10.1259/dmfr.20190107.
  • Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E. Deep‐learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019; 35:301–307. DOI:10.1007/s11282-018-0363-7.
  • Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. 2018; 24:236–241. DOI: 10.4258/hir.2018.24.3.236.
  • Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endod. 2019; 45:917–922. DOI: 10.1016/j.joen.2019.03.016.
  • Deyer T, Doshi A. Application of artificial intelligence to radiology. Ann Transl Med. 2019; 7:230. DOI: 10.21037/atm.2019.05.79.
  • Neri E, de Souza N, Brady A, Bayarri AA, Becker CD, Coppola F, Visser J -European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging. 2019;10(1):44. DOI: 10.1186/s13244-019-0738-2.
  • Syed AB, Zoga AC. Artificial intelligence in radiology: current technology and future directions. Semin Musculoskelet Radiol. 2018; 22:540–545. DOI: 10.1055/s-0038-1673383.
  • Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, Huh KH, Lee SS, Heo MS, Choi SC, Kim TI, Yi WJ. Deep leaming hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020; 10:753. DOI: 10.1038/s41598-020-64509-z.
  • Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician confidence in artificial intelligence: An online mobile survey. J Med Internet Res 2019; 21: e12422. DOI: 10.2196/12422.
  • Kılıc MC, Bayrakdar IS, Çelik O, Bilgir E, Orhan K, Aydın OB, Kaplan FA, Saglam H, Odabas A, Aslan AF, Yılmaz AB. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021 ;50(6):20200172. DOI: 10.1259/dmfr.20200172.
  • Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Sci Dent. 2020;50(2):169-174. DOI: 10.5624/isd.2020.50.2.169.
  • Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: A survey. Imaging Sci Dent. 2020 ;50(3):193-198. DOI: 10.5624/isd.2020.50.3.193.
  • Alsharqi M, Woodward WJ, Mumith JA, Markham DC, Upton R, Leeson P. Artificial intelligence and echocardiography. Echo Res Pract 2018; 5: R115-25. DOI: 10.1530/ERP-18-0056.
  • Wang S, Summers RM. Machine leaming and radiology. Medical Image Analysis 2012; 16:933-951. DOI: 10.1016/j.media.2012.02.005.
  • Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019; 49:1-7. DOI: 10.5624/isd.2019.49.1.1.
  • Silva G, Oliveira L, Pithon M. Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspective. Expert Systems with Applications 2018;107:15–31.DOI: 10.1016/j.eswa.2018.04.001.
  • Koch T, Perslev M, Igel C, Brandt S. Accurate segmentation of dental panoramic radiographs with unets. International Symposium on Biomedical Imaging. IEEE. 2019; 15–19. DOI:10.1109/ISBI.2019.8759563
  • Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic x-ray images. Conference on Graphics, Patterns and Images IEEE. 2018; 400–407. DOI: 10.1109/SIBGRAPI.2018.00058.
  • Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019; 48:20180051. DOI: 10.1259/dmfr.20180051.
  • Celik O, Odabas A, Bayrakdar IS, Bilgir E, Akkoca F. The detection of tooth deficiency on panoramic radiography using deep learning technique: An artificial ıntelligence pilot study. Selcuk Dental Journal 2019; 6: 168-172.
  • Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464-469. DOI: 10.1016/j.oooo.2020.04.813.
  • Mine Y, Iwamoto Y, Okazaki S, Nakamura K, Takeda S, Peng TY, Mitsuhata C, Kakimoto N, Kozai K, Murayama T. Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study. Int J Paediatr Dent. 2022;32(5):678-685. DOI: 10.1111/ipd.12946
  • Prados-Privado M, García Villalón J, Blázquez Torres A, Martínez-Martínez CH, Ivorra C. A convolutional neural network for automatic tooth numbering in panoramic images. Biomed Res Int. 2021; 2021:3625386. DOI:10.1155/2021/3625386.

Yıl 2023, Cilt: 13 Sayı: 4, 883 - 888, 29.12.2023

Doğaçhan Mertoğlu Gaye Keser Filiz Mediha Namdar Pekiner İbrahim Şevki Bayrakdar Özer Çelik Kaan Orhan

https://doi.org/10.33808/clinexphealthsci.1219160

Öz

Anahtar Kelimeler

Artificial intelligence deep learning panoramic radiograph tooth numbering

Kaynakça

  • Shah N, Bansal N, Logani A. Recent advances in imaging technologies in dentistry. World J Radiol. 2014;6(10):794-807. DOI:10.4329/wjr.v6.i10.794.
  • Choi JW. Assessment of panoramic radiography as a national oral examination tool: Review of the literature. Imaging Sci Dent. 2011; 41:1–6. DOI: 10.5624/isd.2011.41.1.1.
  • Bilgir E, Bayrakdar IS, Celik O, Orhan K, Akkoca F, Saglam H, Odabas A, Aslan AF, Ozcetin C, Kıllı M, Rozylo-Kalinowska I. An artifıcial intelligence approach to automatic tooth detection and numbering in panoramic radiographs. BMC Med Imaging. 2021; 21:124. DOI: 10.1186/s12880-021-00656-7.
  • Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res. 2020; 99:769-774. DOI: 10.1177/0022034520915714.
  • Kim J, Lee HS, Song IS, Jung KH. DeNTNet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs. Sci Rep. 2019; 9:17615. DOI: 10.1038/s41598-019-53758-2.
  • Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol. 2020; 49:20190107. DOI: 10.1259/dmfr.20190107.
  • Murata M, Ariji Y, Ohashi Y, Kawai T, Fukuda M, Funakoshi T, Kise Y, Nozawa M, Katsumata A, Fujita H, Ariji E. Deep‐learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol. 2019; 35:301–307. DOI:10.1007/s11282-018-0363-7.
  • Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthc Inform Res. 2018; 24:236–241. DOI: 10.4258/hir.2018.24.3.236.
  • Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Deep learning for the radiographic detection of apical lesions. J Endod. 2019; 45:917–922. DOI: 10.1016/j.joen.2019.03.016.
  • Deyer T, Doshi A. Application of artificial intelligence to radiology. Ann Transl Med. 2019; 7:230. DOI: 10.21037/atm.2019.05.79.
  • Neri E, de Souza N, Brady A, Bayarri AA, Becker CD, Coppola F, Visser J -European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging. 2019;10(1):44. DOI: 10.1186/s13244-019-0738-2.
  • Syed AB, Zoga AC. Artificial intelligence in radiology: current technology and future directions. Semin Musculoskelet Radiol. 2018; 22:540–545. DOI: 10.1055/s-0038-1673383.
  • Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, Huh KH, Lee SS, Heo MS, Choi SC, Kim TI, Yi WJ. Deep leaming hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020; 10:753. DOI: 10.1038/s41598-020-64509-z.
  • Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician confidence in artificial intelligence: An online mobile survey. J Med Internet Res 2019; 21: e12422. DOI: 10.2196/12422.
  • Kılıc MC, Bayrakdar IS, Çelik O, Bilgir E, Orhan K, Aydın OB, Kaplan FA, Saglam H, Odabas A, Aslan AF, Yılmaz AB. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021 ;50(6):20200172. DOI: 10.1259/dmfr.20200172.
  • Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Sci Dent. 2020;50(2):169-174. DOI: 10.5624/isd.2020.50.2.169.
  • Sur J, Bose S, Khan F, Dewangan D, Sawriya E, Roul A. Knowledge, attitudes, and perceptions regarding the future of artificial intelligence in oral radiology in India: A survey. Imaging Sci Dent. 2020 ;50(3):193-198. DOI: 10.5624/isd.2020.50.3.193.
  • Alsharqi M, Woodward WJ, Mumith JA, Markham DC, Upton R, Leeson P. Artificial intelligence and echocardiography. Echo Res Pract 2018; 5: R115-25. DOI: 10.1530/ERP-18-0056.
  • Wang S, Summers RM. Machine leaming and radiology. Medical Image Analysis 2012; 16:933-951. DOI: 10.1016/j.media.2012.02.005.
  • Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019; 49:1-7. DOI: 10.5624/isd.2019.49.1.1.
  • Silva G, Oliveira L, Pithon M. Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspective. Expert Systems with Applications 2018;107:15–31.DOI: 10.1016/j.eswa.2018.04.001.
  • Koch T, Perslev M, Igel C, Brandt S. Accurate segmentation of dental panoramic radiographs with unets. International Symposium on Biomedical Imaging. IEEE. 2019; 15–19. DOI:10.1109/ISBI.2019.8759563
  • Jader G, Fontineli J, Ruiz M, Abdalla K, Pithon M, Oliveira L. Deep instance segmentation of teeth in panoramic x-ray images. Conference on Graphics, Patterns and Images IEEE. 2018; 400–407. DOI: 10.1109/SIBGRAPI.2018.00058.
  • Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, Sveshnikov MM, Bednenko GB. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019; 48:20180051. DOI: 10.1259/dmfr.20180051.
  • Celik O, Odabas A, Bayrakdar IS, Bilgir E, Akkoca F. The detection of tooth deficiency on panoramic radiography using deep learning technique: An artificial ıntelligence pilot study. Selcuk Dental Journal 2019; 6: 168-172.
  • Kuwada C, Ariji Y, Fukuda M, Kise Y, Fujita H, Katsumata A, Ariji E. Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol. 2020;130(4):464-469. DOI: 10.1016/j.oooo.2020.04.813.
  • Mine Y, Iwamoto Y, Okazaki S, Nakamura K, Takeda S, Peng TY, Mitsuhata C, Kakimoto N, Kozai K, Murayama T. Detecting the presence of supernumerary teeth during the early mixed dentition stage using deep learning algorithms: A pilot study. Int J Paediatr Dent. 2022;32(5):678-685. DOI: 10.1111/ipd.12946
  • Prados-Privado M, García Villalón J, Blázquez Torres A, Martínez-Martínez CH, Ivorra C. A convolutional neural network for automatic tooth numbering in panoramic images. Biomed Res Int. 2021; 2021:3625386. DOI:10.1155/2021/3625386.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ağız, Diş ve Çene Radyolojisi
Bölüm Articles
Yazarlar

Doğaçhan Mertoğlu 0000-0002-1941-1593

Gaye Keser 0000-0001-7564-4757

Filiz Mediha Namdar Pekiner 0000-0001-7426-5587

İbrahim Şevki Bayrakdar 0000-0001-5036-9867

Özer Çelik 0000-0002-4409-3101

Kaan Orhan 0000-0001-6768-0176

Yayımlanma Tarihi 29 Aralık 2023
Gönderilme Tarihi 18 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 4

Kaynak Göster

APA Mertoğlu, D., Keser, G., Namdar Pekiner, F. M., Bayrakdar, İ. Ş., vd. (2023). A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study. Clinical and Experimental Health Sciences, 13(4), 883-888. https://doi.org/10.33808/clinexphealthsci.1219160
AMA Mertoğlu D, Keser G, Namdar Pekiner FM, Bayrakdar İŞ, Çelik Ö, Orhan K. A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study. Clinical and Experimental Health Sciences. Aralık 2023;13(4):883-888. doi:10.33808/clinexphealthsci.1219160
Chicago Mertoğlu, Doğaçhan, Gaye Keser, Filiz Mediha Namdar Pekiner, İbrahim Şevki Bayrakdar, Özer Çelik, ve Kaan Orhan. “A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study”. Clinical and Experimental Health Sciences 13, sy. 4 (Aralık 2023): 883-88. https://doi.org/10.33808/clinexphealthsci.1219160.
EndNote Mertoğlu D, Keser G, Namdar Pekiner FM, Bayrakdar İŞ, Çelik Ö, Orhan K (01 Aralık 2023) A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study. Clinical and Experimental Health Sciences 13 4 883–888.
IEEE D. Mertoğlu, G. Keser, F. M. Namdar Pekiner, İ. Ş. Bayrakdar, Ö. Çelik, ve K. Orhan, “A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study”, Clinical and Experimental Health Sciences, c. 13, sy. 4, ss. 883–888, 2023, doi: 10.33808/clinexphealthsci.1219160.
ISNAD Mertoğlu, Doğaçhan vd. “A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study”. Clinical and Experimental Health Sciences 13/4 (Aralık 2023), 883-888. https://doi.org/10.33808/clinexphealthsci.1219160.
JAMA Mertoğlu D, Keser G, Namdar Pekiner FM, Bayrakdar İŞ, Çelik Ö, Orhan K. A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study. Clinical and Experimental Health Sciences. 2023;13:883–888.
MLA Mertoğlu, Doğaçhan vd. “A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study”. Clinical and Experimental Health Sciences, c. 13, sy. 4, 2023, ss. 883-8, doi:10.33808/clinexphealthsci.1219160.
Vancouver Mertoğlu D, Keser G, Namdar Pekiner FM, Bayrakdar İŞ, Çelik Ö, Orhan K. A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study. Clinical and Experimental Health Sciences. 2023;13(4):883-8.
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