Öz
Amaç: Bu çalışmanın amacı yapay zeka (Artificial Intelligence) (AI) sistemleri kullanılarak dental panoramik radyografik görüntülerden alveoler kemik kaybını tespit etmektir.
Gereç ve Yöntem: Bu çalışmada toplam 2276 panoramik radyografik görüntü kullanıldı. Bunların 1137'si kemik yıkımı olan vakalara aitken, 1139'u periodontal olarak sağlıklıydı. Veri kümesi eğitim (n = 1856), doğrulama (n = 210) ve test seti (n = 210) olarak üç bölüme ayrıldı. Veri setindeki tüm görüntüler eğitimden önce 1472x718 piksel olarak yeniden boyutlandırıldı. Açık kaynaklı python programlama dili ve OpenCV, NumPy, Pandas ve Matplotlib kütüphaneleri etkili bir şekilde kullanılarak rastgele bir dizi oluşturuldu. Ön işleme için önceden eğitilmiş bir Google Net Inception v3 CNN ağı kullanılmış ve veri setleri aktarım öğrenimi kullanılarak eğitildi. Tanısal performans, duyarlılık, özgüllük, kesinlik, doğruluk ve F1 skoru kullanılarak konfüzyon matrisi ile değerlendirildi.
Bulgular: Kemik kaybı olan 105 olgunun 99'u AI sistemi ile tespit edildi. Duyarlılık 0.94, özgüllük 0.88, hassasiyet 0.89, doğruluk 0.91 ve F1 skoru 0.91 idi.
Sonuç: Konvolüsyon nöral ağ modeli periodontal kemik kayıplarını belirlemede başarılıdır. Gelecekte tanı ve tedavi planlamasında hekimlerin çalışmasını kolaylaştıran bir sistem olarak kullanılabilir.
Anahtar Kelimeler
yapay zeka panoramik radyografi alveolar kemik kaybı periodontitis periodontoloji derin öğrenme
Kaynakça
- 1. Dentino A, Lee S, Mailhot J, Hefti AF. Principles of periodontology. Periodontology 2000 2013; 61:16-53.
- 2. Mol A. Imaging methods in periodontology. Periodontology 2000 2004; 34:34-48.
- 3. Tonetti MS, Jepsen S, Jin L, Otomo‐Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. Journal of clinical periodontology 2017; 44:456-462.
- 4. Clerehugh V, Tugnait A. Diagnosis and management of periodontal diseases in children and adolescents. Periodontology 2000 2001; 26:146-168.
- 5. Scarfe WC, Azevedo B, Pinheiro LR, Priaminiarti M, Sales MA. The emerging role of maxillofacial radiology in the diagnosis and management of patients with complex periodontitis. Periodontology 2000 2017; 74:116-139.
- 6. Rushton V, Horner K. The use of panoramic radiology in dental practice. Journal of dentistry 1996; 24:185-201.
- 7. Kaimenyi J, Ashley F. Assessment of bone loss in periodontitis from panoramic radiographs. Journal of clinical periodontology 1988; 15:170-174.
- 8. Chartrand G, Cheng PM, Vorontsov Eet al. Deep learning: a primer for radiologists. Radiographics 2017; 37:2113-2131.
- 9. Moutselos K, Berdouses E, Oulis C, Maglogiannis I. Recognizing Occlusal Caries in Dental Intraoral Images Using Deep Learning 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, 2019:1617-1620.
- 10. Fukuda M, Inamoto K, Shibata Net al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology 2019:1-7.
- 11. Hiraiwa T, Ariji Y, Fukuda Met al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology 2019; 48:20180218.
- 12. Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International Endodontic Journal 2020; 53:680-689.
- 13. Aberin STA, de Goma JC. Detecting Periodontal Disease Using Convolutional Neural Networks 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM): IEEE, 2018:1-6.
- 14. Lee J-H, Kim D-h, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal & implant science 2018; 48:114-123.
- 15. Krois J, Ekert T, Meinhold Let al. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports 2019; 9:1-6.
- 16. Kim J, Lee H-S, Song I-S, Jung K-H. DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Scientific reports 2019; 9:1-9.
- 17. Chang H-J, Lee S-J, Yong T-Het al. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Scientific Reports 2020; 10:1-8.
- 18. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 2008; 106:879-884.
- 19. Valizadeh S, Goodini M, Ehsani S, Mohseni H, Azimi F, Bakhshandeh H. Designing of a computer software for detection of approximal caries in posterior teeth. Iranian Journal of Radiology 2015; 12.
- 20. Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofacial Radiology 2017; 46:20160107.
- 21. Kositbowornchai S, Plermkamon S, Tangkosol T. Performance of an artificial neural network for vertical root fracture detection: an ex vivo study. Dental traumatology 2013; 29:151-155.
- 22. Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthcare informatics research 2018; 24:236-241.
- 23. Lee K-S, Ryu J-J, Jang HS, Lee D-Y, Jung S-K. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Applied Sciences 2020; 10:2124.
- 24. Chen H, Zhang K, Lyu Pet al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports 2019; 9:1-11.
- 25. Tuzoff DV, Tuzova LN, Bornstein MMet al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology 2019; 48:20180051.
- 26. Tang Z, Liu X, Chen K. Comparison of digital panoramic radiography versus cone beam computerized tomography for measuring alveolar bone. Head & face medicine 2017; 13:1-7.
- 27. Ozden F, Ozgonenel O, Ozden B, Aydogdu A. Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Nigerian journal of clinical practice 2015; 18:416-421.
- 28. Fukuda M, Ariji Y, Kise Yet al. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 2020.
- 29. Muramatsu C, Kutsuna S, Takahashi Ret al. Tooth numbering in cone-beam CT using a relation network for automatic filing of dentition charts Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications: International Society for Optics and Photonics, 2020:113180L.
- 30. Balaei AT, de Chazal P, Eberhard J, Domnisch H, Spahr A, Ruiz K. Automatic detection of periodontitis using intra-oral images 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, 2017:3906-3909.
- 31. Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Science in Dentistry 2020; 50:169-174.
SUCCESS OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETERMINING ALVEOLAR BONE LOSS FROM DENTAL PANORAMIC RADIOGRAPHY IMAGES
Öz
Objectives: The aim of this study was to detect alveolar bone loss from dental panoramic radiographic images using artificial intelligence systems.
Material and Methods: A total of 2276 panoramic radiographic images were used in this study. While 1137 of them belong to cases with bone destruction, 1139 were periodontally healthy. The dataset is divided into three parts as training (n=1856) , validation (n=210) and testing set (n= 210). All images in the data set were resized to 1472x718 pixels before training. A random sequence was created using the open-source python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries effectively. A pre-trained Google Net Inception v3 CNN network was used for preprocessing and data sets were trained using transfer learning. Diagnostic performance was evaluated with the confusion matrix using sensivitiy, specificity, precision, accuracy and F1 score.
Results: Of the 105 cases with bone loss, 99 were detected by the AI system. Sensitivity was 0.94, specificity 0.88, precision 0.89, accuracy 0.91 and F1 score 0.91.
Conclusion: The convolutional neural network model is successful in determining periodontal bone losses. It can be used as a system to facilitate the work of physicians in diagnosis and treatment planning in the future.
Anahtar Kelimeler
panoramic radiography artificial intelligence alveolar bone loss periodontitis periodontology deep learning
Kaynakça
- 1. Dentino A, Lee S, Mailhot J, Hefti AF. Principles of periodontology. Periodontology 2000 2013; 61:16-53.
- 2. Mol A. Imaging methods in periodontology. Periodontology 2000 2004; 34:34-48.
- 3. Tonetti MS, Jepsen S, Jin L, Otomo‐Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. Journal of clinical periodontology 2017; 44:456-462.
- 4. Clerehugh V, Tugnait A. Diagnosis and management of periodontal diseases in children and adolescents. Periodontology 2000 2001; 26:146-168.
- 5. Scarfe WC, Azevedo B, Pinheiro LR, Priaminiarti M, Sales MA. The emerging role of maxillofacial radiology in the diagnosis and management of patients with complex periodontitis. Periodontology 2000 2017; 74:116-139.
- 6. Rushton V, Horner K. The use of panoramic radiology in dental practice. Journal of dentistry 1996; 24:185-201.
- 7. Kaimenyi J, Ashley F. Assessment of bone loss in periodontitis from panoramic radiographs. Journal of clinical periodontology 1988; 15:170-174.
- 8. Chartrand G, Cheng PM, Vorontsov Eet al. Deep learning: a primer for radiologists. Radiographics 2017; 37:2113-2131.
- 9. Moutselos K, Berdouses E, Oulis C, Maglogiannis I. Recognizing Occlusal Caries in Dental Intraoral Images Using Deep Learning 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, 2019:1617-1620.
- 10. Fukuda M, Inamoto K, Shibata Net al. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiology 2019:1-7.
- 11. Hiraiwa T, Ariji Y, Fukuda Met al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology 2019; 48:20180218.
- 12. Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Özyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans. International Endodontic Journal 2020; 53:680-689.
- 13. Aberin STA, de Goma JC. Detecting Periodontal Disease Using Convolutional Neural Networks 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM): IEEE, 2018:1-6.
- 14. Lee J-H, Kim D-h, Jeong S-N, Choi S-H. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal & implant science 2018; 48:114-123.
- 15. Krois J, Ekert T, Meinhold Let al. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports 2019; 9:1-6.
- 16. Kim J, Lee H-S, Song I-S, Jung K-H. DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Scientific reports 2019; 9:1-9.
- 17. Chang H-J, Lee S-J, Yong T-Het al. Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Scientific Reports 2020; 10:1-8.
- 18. Devito KL, de Souza Barbosa F, Felippe Filho WN. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 2008; 106:879-884.
- 19. Valizadeh S, Goodini M, Ehsani S, Mohseni H, Azimi F, Bakhshandeh H. Designing of a computer software for detection of approximal caries in posterior teeth. Iranian Journal of Radiology 2015; 12.
- 20. Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofacial Radiology 2017; 46:20160107.
- 21. Kositbowornchai S, Plermkamon S, Tangkosol T. Performance of an artificial neural network for vertical root fracture detection: an ex vivo study. Dental traumatology 2013; 29:151-155.
- 22. Poedjiastoeti W, Suebnukarn S. Application of convolutional neural network in the diagnosis of jaw tumors. Healthcare informatics research 2018; 24:236-241.
- 23. Lee K-S, Ryu J-J, Jang HS, Lee D-Y, Jung S-K. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Applied Sciences 2020; 10:2124.
- 24. Chen H, Zhang K, Lyu Pet al. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports 2019; 9:1-11.
- 25. Tuzoff DV, Tuzova LN, Bornstein MMet al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology 2019; 48:20180051.
- 26. Tang Z, Liu X, Chen K. Comparison of digital panoramic radiography versus cone beam computerized tomography for measuring alveolar bone. Head & face medicine 2017; 13:1-7.
- 27. Ozden F, Ozgonenel O, Ozden B, Aydogdu A. Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Nigerian journal of clinical practice 2015; 18:416-421.
- 28. Fukuda M, Ariji Y, Kise Yet al. Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 2020.
- 29. Muramatsu C, Kutsuna S, Takahashi Ret al. Tooth numbering in cone-beam CT using a relation network for automatic filing of dentition charts Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications: International Society for Optics and Photonics, 2020:113180L.
- 30. Balaei AT, de Chazal P, Eberhard J, Domnisch H, Spahr A, Ruiz K. Automatic detection of periodontitis using intra-oral images 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): IEEE, 2017:3906-3909.
- 31. Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Science in Dentistry 2020; 50:169-174.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Original Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 31 Aralık 2020 |
| Gönderilme Tarihi | 4 Ağustos 2020 |
| Yayımlandığı Sayı | Yıl 2020Cilt: 23 Sayı: 4 |
Cited By
Accuracy of convolutional neural network in the diagnosis of alveolar bone loss due to periodontal disease: A systematic review and meta-analysis
Journal of Datta Meghe Institute of Medical Sciences University
https://doi.org/10.4103/jdmimsu.jdmimsu_281_22
Development and validation of an artificial intelligence software for periodontal bone loss in panoramic imaging
International Journal of Imaging Systems and Technology
https://doi.org/10.1002/ima.22973
Deep learning in periodontology and oral implantology: A scoping review
Journal of Periodontal Research
https://doi.org/10.1111/jre.13037
C.E. Credit. Artificial Intelligence Applications for the Radiographic Detection of Periodontal Disease: A Scoping Review
Journal of the California Dental Association
https://doi.org/10.1080/19424396.2023.2206301
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