AIを用いたパノラマX線画像からのカルテ入力支援システムの開発
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| Published in | 歯科放射線 Vol. 62; no. 1; pp. 24 - 34 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
特定非営利活動法人 日本歯科放射線学会
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0389-9705 2185-6311 |
| DOI | 10.11242/dentalradiology.62.24 |
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| Author | 北, 研二 誉田, 栄一 鳥井, 浩平 |
|---|---|
| Author_xml | – sequence: 1 fullname: 北, 研二 organization: 徳島大学大学院社会産業理工学研究部 – sequence: 1 fullname: 鳥井, 浩平 organization: 徳島大学大学院先端技術科学教育部 – sequence: 1 fullname: 誉田, 栄一 organization: 徳島大学大学院医歯薬学研究部 |
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| Copyright | 2022 特定非営利活動法人 日本歯科放射線学会 |
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| References | 22. Winter GB. Principles of Exodontia as Applied to the Impacted Mandibular Tird Molar: A Complete Treatise on the Operative Technic with Clinical Diagnoses and Radiographic Interpretations. American Medical Books. 1926. 5. Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, Zha Y, Liang W, Wang C, Wang K, Ye L, Gao M, Zhou Z, Li L, Wang J, Yang Z, Cai H, Xu J, Yang L, Cai W, Xu W, Wu S, Zhang W, Jiang S, Zheng L, Zhang X, Wang L, Lu L, Li J, Yin H, Wang W, Li O, Zhang C, Liang L, Wu T, Deng R, Wei K, Zhou Y, Chen T, Lau JY, Fok M, He J, Lin T, Li W, Wang G. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of Covid-19 Pneumonia Using Computed Tomography. Cell. 2020；181：1423-1433. 10. Zhu H, Cao Z, Lian L, Ye G, Gao H, Wu J. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Computing and Applications. 2022. doi: 10.1007/s00521-021-06684-2 15. Abdulla W. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub Repository. 2017. https://github.com/matterport/Mask_RCNN 12. 志村一男．理想のX線画像を目指して—マルチ周波数処理について—．日本放射線技術学会雑誌．2001；57（7）：796-802. 8. 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. Dentomaxillofacial Radiology. 2019. doi: 10.1259/dmfr.20180051 16. Suzuki S, Abe K. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing. 1985；30：32-46. 18. Ridnik T, Ben-Baruch E, Noy A, Zelnik L. ImageNet-21K Pretraining for the Masses. NeurIPS Datasets and Benchmarks. 2021. doi: 10.48550/arXiv.2104.10972 19. Kingma DP, Adam JB. A Method for Stochastic Optimization. International Conference for Learning Representations. 2015. doi: 10.48550/arXiv.1412.6980 3. Zhao W, Jiang W, Qiu X. Deep Learning for COVID-19 detection based on CT images. Nature Scientific Reports. 2021；11：14353. 1. Ferrucci DA. Introduction to “This is Watson”. IBM Journal of Research and Development. 2012；56：1-15. 17. Tan M, Le Q. EfficientNetV2: Smaller Models and Faster Training. International Conference on Machine Learning. International Conference on Machine Learning. 2021. doi: 10.48550/arXiv.2104.00298 7. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Nature Scientific Reports. 2019；9：3840. 14. Lin TY, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Dollár P. Microsoft COCO: Common Objects in Context. European Conference on Computer Vision. 2014. doi: 10.48550/arXiv.1405.0312 23. Sukegawa S, Matsuyama T, Tanaka F, Hara T, Yoshii K, Yamashita K, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Evaluation of multi‑task learning in deep learning‑based positioning classifcation of mandibular third molars. Nature Scientific Reports. 2022；12：684. 13. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. Computer Vision and Pattern Recognition. 2017. doi: 10.48550/arXiv.1703.06870 21. Pell GJ, Gregory GT. Impacted Mandibular Third Molars: Classification and Modified Technique for Removal. Dental Digest. 1933；39（9）：330-338. 9. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry. 2018；77：106-111. 2. Erdaw Y, Tachbele E. Machine Learning Model Applied on Chest X-Ray Images Enables Automatic Detection of COVID-19 Cases with High Accuracy. International Journal of General Medicine. 2021；14：4923-4931. 24. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations. 2020. doi: 0.48550/arXiv.2010.11929 20. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X. TensorFlow: A system for large-scale machine learning. arXiv preprint. 2016. doi: 10.48550/arXiv.1605.08695 6. Panetta K, Rajendran R, Ramesh A, Rao S, Agaian S. Tufts Dental Database: A Multimodal Panoramic X-ray Dataset for Benchmarking Diagnostic Systems. Institute of Electrical and Electronics Engineers, Journal of Biomedical Health Informatics. 2021. doi: 10.1109/JBHI.2021.3117575 4. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Conference on Computer Vision and Pattern Recognition. 2017. doi: 10.1109/CVPR.2017.369 11. Wada K. Labelme: Image Polygonal Annotation with Python. GitHub Repository. 2021. doi: 10.5281/zenodo.5711226 |
| References_xml | – reference: 15. Abdulla W. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub Repository. 2017. https://github.com/matterport/Mask_RCNN – reference: 2. Erdaw Y, Tachbele E. Machine Learning Model Applied on Chest X-Ray Images Enables Automatic Detection of COVID-19 Cases with High Accuracy. International Journal of General Medicine. 2021；14：4923-4931. – reference: 3. Zhao W, Jiang W, Qiu X. Deep Learning for COVID-19 detection based on CT images. Nature Scientific Reports. 2021；11：14353. – reference: 4. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Conference on Computer Vision and Pattern Recognition. 2017. doi: 10.1109/CVPR.2017.369 – reference: 7. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Nature Scientific Reports. 2019；9：3840. – reference: 12. 志村一男．理想のX線画像を目指して—マルチ周波数処理について—．日本放射線技術学会雑誌．2001；57（7）：796-802. – reference: 6. Panetta K, Rajendran R, Ramesh A, Rao S, Agaian S. Tufts Dental Database: A Multimodal Panoramic X-ray Dataset for Benchmarking Diagnostic Systems. Institute of Electrical and Electronics Engineers, Journal of Biomedical Health Informatics. 2021. doi: 10.1109/JBHI.2021.3117575 – reference: 11. Wada K. Labelme: Image Polygonal Annotation with Python. GitHub Repository. 2021. doi: 10.5281/zenodo.5711226 – reference: 22. Winter GB. Principles of Exodontia as Applied to the Impacted Mandibular Tird Molar: A Complete Treatise on the Operative Technic with Clinical Diagnoses and Radiographic Interpretations. American Medical Books. 1926. – reference: 24. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations. 2020. doi: 0.48550/arXiv.2010.11929 – reference: 1. Ferrucci DA. Introduction to “This is Watson”. IBM Journal of Research and Development. 2012；56：1-15. – reference: 16. Suzuki S, Abe K. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing. 1985；30：32-46. – reference: 17. Tan M, Le Q. EfficientNetV2: Smaller Models and Faster Training. International Conference on Machine Learning. International Conference on Machine Learning. 2021. doi: 10.48550/arXiv.2104.00298 – reference: 20. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X. TensorFlow: A system for large-scale machine learning. arXiv preprint. 2016. doi: 10.48550/arXiv.1605.08695 – reference: 19. Kingma DP, Adam JB. A Method for Stochastic Optimization. International Conference for Learning Representations. 2015. doi: 10.48550/arXiv.1412.6980 – reference: 5. Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, Zha Y, Liang W, Wang C, Wang K, Ye L, Gao M, Zhou Z, Li L, Wang J, Yang Z, Cai H, Xu J, Yang L, Cai W, Xu W, Wu S, Zhang W, Jiang S, Zheng L, Zhang X, Wang L, Lu L, Li J, Yin H, Wang W, Li O, Zhang C, Liang L, Wu T, Deng R, Wei K, Zhou Y, Chen T, Lau JY, Fok M, He J, Lin T, Li W, Wang G. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of Covid-19 Pneumonia Using Computed Tomography. Cell. 2020；181：1423-1433. – reference: 10. Zhu H, Cao Z, Lian L, Ye G, Gao H, Wu J. CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image. Neural Computing and Applications. 2022. doi: 10.1007/s00521-021-06684-2 – reference: 8. 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. Dentomaxillofacial Radiology. 2019. doi: 10.1259/dmfr.20180051 – reference: 9. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry. 2018；77：106-111. – reference: 18. Ridnik T, Ben-Baruch E, Noy A, Zelnik L. ImageNet-21K Pretraining for the Masses. NeurIPS Datasets and Benchmarks. 2021. doi: 10.48550/arXiv.2104.10972 – reference: 13. He K, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. Computer Vision and Pattern Recognition. 2017. doi: 10.48550/arXiv.1703.06870 – reference: 23. Sukegawa S, Matsuyama T, Tanaka F, Hara T, Yoshii K, Yamashita K, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Evaluation of multi‑task learning in deep learning‑based positioning classifcation of mandibular third molars. Nature Scientific Reports. 2022；12：684. – reference: 14. Lin TY, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Dollár P. Microsoft COCO: Common Objects in Context. European Conference on Computer Vision. 2014. doi: 10.48550/arXiv.1405.0312 – reference: 21. Pell GJ, Gregory GT. Impacted Mandibular Third Molars: Classification and Modified Technique for Removal. Dental Digest. 1933；39（9）：330-338. |
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| SubjectTerms | パノラマX線画像 人工知能 歯科支援システム 画像認識 |
| Title | AIを用いたパノラマX線画像からのカルテ入力支援システムの開発 |
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