Medical Informatics III

EnglishTürkçe

CHAPTER


DOI :10.26650/B/ET07.2023.005.23   IUP :10.26650/B/ET07.2023.005.23    Full Text (PDF)

Application of Deep Learning in Dentistry

Ayşem Yurtseven Günay, Sabire Değer İşler, Gülsüm Ak

Deep learning algorithms have become more and more remarkable in medicine and dentistry, especially with their diagnostic success. There are studies on both diagnosis and prognosis in many fields of dentistry. Our aim is to evaluate the results of studies on deep learning in dentistry and to shed light on the future. Studies on deep learning are mostly related to diagnosis and treatment planning based on radiological images. There are studies in areas such as the evaluation of dental plaque formation, the diagnosis of gingivitis and periodontitis through clinical images. Estimates similar to expert opinions were obtained on the diagnosis of dental caries and the need for restorative or endodontic treatment. There are studies on the planning and determination of restorations using clinical images. In oral surgery, there are studies reported in areas such as anatomical formations that should be considered in the surgery of third molars, evaluation of patients for orthognathic surgery, and dental implant application. In addition, studies have been carried out in many different fields of dentistry, such as the diagnosis of malocclusions and the evaluation of cephalometric points, the diagnosis of oral cancers and the prediction of survival of patients. Studies have some disadvantages when compared with other studies related to artificial intelligence. For example, datasets are not very large in the medical field. In addition, barriers to data protection cause this number to decrease further. Moreover, it can be said that they are partially biased because their records obtained from clinics, and therefore data that are not randomly obtained from the community. Another disadvantage arises from the complexity of the clinical decision-making process. Although there is no big difference in diagnosis; many factors are effective in treatment planning and anamnesis, clinical images, radiographic images should be together to evaluate all factors. Although there are some disadvantages at the current stage, studies on deep learning are increasing rapidly. When the studies are evaluated, we think that deep learning will take place effectively in dentistry practices in the near future.


Keywords: Deep learning, dentistry, artificial intelligence
DOI :10.26650/B/ET07.2023.005.23   IUP :10.26650/B/ET07.2023.005.23    Full Text (PDF)

Derin Öğrenmenin Diş Hekimliğinde Kullanımı

Ayşem Yurtseven Günay, Sabire Değer İşler, Gülsüm Ak

Derin öğrenme algoritmaları, özellikle tanısal başarıları ile tıp ve diş hekimliğinde her geçen gün daha dikkat çekici bir hale gelmiştir. Diş hekimliğinin pek çok alanında hem tanı koyma hem de prognoz belirleme üzerine yapılmış çalışmalar mevcuttur. Bizim amacımız diş hekimliğinde derin öğrenme ile ilgili yapılan çalışmaların sonuçlarını değerlendirmek ve geleceğe ışık tutmaktır. Derin öğrenme ile ilgili yapılan çalışmalar büyük çoğunlukla radyolojik görüntüler üzerinden tanı ve tedavi planlamasının yapılması ile ilgilidir. Klinik görseller üzerinden de dental plak oluşumunun değerlendirilmesi, gingivitis ve periodontitisin tanısı gibi alanlarda yapılan çalışmalar mevcuttur. Diş çürüğünün tanısının koyulması ve restoratif ya da endodontik tedavi ihtiyacı üzerine de uzman görüşlerine benzer tahminler elde edilmiştir. Protetik diş tedavisinde planlama ve restorasyonların tespit edilmesi ile ilgili yine klinik görseller kullanılarak yapılan çalışmalar mevcuttur. Oral cerrahide yirmi yaş dişlerinin cerrahisinde dikkat edilmesi gereken anatomik oluşumlar, ortognatik cerrahi için hastaların değerlendirilmesi, dental implant uygulaması gibi alanlarda bildirilen araştırmalar bulunmaktadır. Bu çalışmalara ek olarak maloklüzyonların tanısının koyulması ve sefalometrik noktaların değerlendirilmesi, oral kanserlerin tanısı ve hastaların sağ kalım tahminleri gibi farklı pek çok diş hekimliği alanında çalışmalar yapılmıştır. Yapılan çalışmaların yapay zeka ile ilişkili diğer çalışmalar ile karşılaştırıldığında bazı dezavantajları mevcuttur. Örneğin veri kümeleri sağlık alanında çok büyük değildir. Ayrıca verilerin korunması ile ilgili engeller, bu sayının daha da azalmasına sebep olmaktadır. Buna ek olarak kliniklerden alınan kayıtlar oldukları, dolayısıyla toplum içerisinden rastgele alınmayan datalar oldukları için kısmen taraflı oldukları söylenebilir. Bir başka dezavantaj da klinik olarak karar verme sürecinin karmaşık olmasından doğmaktadır. Tanı koymada büyük farklılık olmasa da; tedavi planlamasında pek çok faktör etkendir ve tüm faktörler üzerinden değerlendirme yapmak için anamnez, klinik görseller, radyografik görüntüler bir arada olmalıdır. Günümüzde gelinen aşamada bazı dezavantajları olsa da, derin öğrenme ile ilgili araştırmalar hızla çoğalmaktadır. Yapılan çalışmalar değerlendirildiğinde derin öğrenmenin diş hekimliği uygulamalarında yakın gelecekte etkin bir şekilde yer alacağı düşüncesindeyiz.


Keywords: Derin öğrenme, diş hekimliği, yapay zeka

References

  • Alalharith, D. M., Alharthi, H. M., Alghamdi, W. M., Alsenbel, Y. M., Aslam, N., Khan, I. U., ... Barouch, K. K. google scholar
  • (2020) . A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks. International Journal of Environmental Research and Public Health, 17(22). https://doi.org/10.3390/ijerph17228447 google scholar
  • Ariji, Y., Fukuda, M., Kise, Y., Nozawa, M., Yanashita, Y., Fujita, H., . Ariji, E. (2019). Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 127(5), 458-463. https://doi.org/10.1016Zj.oooo.2018.10.002 google scholar
  • Arık, S. Ö., Ibragimov, B., & Xing, L. (2017). Fully automated quantitative cephalometry using convolutional neural networks. Journal of Medical Imaging (Bellingham, Wash.), 4(1), 014501. https://doi.org/10.1117/1. JMI.4.1.014501 google scholar
  • Braga, M. M., Mendes, F. M., & Ekstrand, K. R. (2010). Detection Activity Assessment and Diagnosis of Dental Caries Lesions. Dental Clinics of North America, 54(3), 479-493. https://doi.org/10.1016/j.cden.2010.03.006 google scholar
  • Burt, J. R., Torosdagli, N., Khosravan, N., RaviPrakash, H., Mortazi, A., Tissavirasingham, F., . Bagci, U. (2018). Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. The British Journal of Radiology, 91(1089), 20170545. https://doi.org/10.1259/bjr.20170545 google scholar
  • Casalegno, F., Newton, T., Daher, R., Abdelaziz, M., Lodi-Rizzini, A., Schürmann, F., . Markram, H. (2019). Caries Detection with Near-Infrared Transillumination Using Deep Learning. Journal of Dental Research, 98(11), 1227-1233. https://doi.org/10.1177/0022034519871884 google scholar
  • Chang, H.-J., Lee, S.-J., Yong, T.-H., Shin, N.-Y., Jang, B.-G., Kim, J.-E., . Yi, W.-J. (2020). Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Scientific Reports, 10(1), 7531. https://doi.org/10.1038/s41598-020-64509-z google scholar
  • Chen, C., Bai, W., Davies, R. H., Bhuva, A. N., Manisty, C. H., Augusto, J. B., . Rueckert, D. (2020). Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images. Frontiers in Cardiovascular Medicine, 7. https://doi.org/10.3389/fcvm.2020.00105 google scholar
  • Chen, H., Zhang, K., Lyu, P., Li, H., Zhang, L., Wu, J., & Lee, C. H. (2019). A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific Reports, 9(1), 1-11. https://doi.org/10.1038/s41598-019-40414-y google scholar
  • Cho, J., Lee, K., Shin, E., Choy, G., & Do, S. (2015). How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? Retrieved from http://arxiv.org/abs/1511.06348 google scholar
  • Choi, H.-I., Jung, S.-K., Baek, S.-H., Lim, W. H., Ahn, S.-J., Yang, I.-H., & Kim, T.-W. (2019). Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery. Journal of Craniofacial Surgery, 30(7), 1986-1989. https://doi.org/10.1097/SCS.0000000000005650 google scholar
  • Corbella, S., Srinivas, S., & Cabitza, F. (2021). Applications of deep learning in dentistry. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 132(2), 225-238. https://doi.org/10.1016/j.oooo.2020.11.003 google scholar
  • Danks, R. P., Bano, S., Orishko, A., Tan, H. J., Moreno Sancho, F., D’Aiuto, F., & Stoyanov, D. (2021). Automating Periodontal bone loss measurement via dental landmark localisation. International Journal of Computer Assisted Radiology and Surgery, 16(7), 1189-1199. https://doi.org/10.1007/s11548-021-02431-z google scholar
  • de Faria Vasconcelos, K., Evangelista, K., Rodrigues, C., Estrela, C., de Sousa, T., & Silva, M. (2012). Detection of periodontal bone loss using cone beam CT and intraoral radiography. Dentomaxillofacial Radiology, 41(1), 64-69. https://doi.org/10.1259/dmfr/13676777 google scholar
  • Ekert, T., Krois, J., Meinhold, L., Elhennawy, K., Emara, R., Golla, T., & Schwendicke, F. (2019). Deep Learning for the Radiographic Detection of Apical Lesions. Journal of Endodontics, 45(7), 917-922.e5. https://doi. org/10.1016/j.joen.2019.03.016 google scholar
  • Ferri, F. F. (2011). Ferri’s Fast Facts in Dermatology A Practical Guide to Skin Diseases and Disorders (J. S. Studdiford & A. Tully, eds.). Philadelphia: Saunders. google scholar
  • Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178(11), 1544. https://doi. org/10.1001/jamainternmed.2018.3763 google scholar
  • Haghanifar, A., Majdabadi, M. M., & Ko, S.-B. (2020). PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier. 1-14. Retrieved from http://arxiv.org/ abs/2012.13666 google scholar
  • Hiraiwa, T., Ariji, Y., Fukuda, M., Kise, Y., Nakata, K., Katsumata, A., ... Ariji, E. (2019). A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology, 48(3), 20180218. https://doi.org/10.1259/dmfr.20180218 google scholar
  • Hosntalab, M., Aghaeizadeh Zoroofi, R., Abbaspour Tehrani-Fard, A., & Shirani, G. (2010). Classification and numbering of teeth in multi-slice CT images using wavelet-Fourier descriptor. International Journal of Computer Assisted Radiology and Surgery, 5(3), 237-249. https://doi.org/10.1007/s11548-009-0389-8 google scholar
  • Hutton, T. (2000). An evaluation of active shape models for the automatic identification of cephalometric landmarks. The European Journal of Orthodontics, 22(5), 499-508. https://doi.org/10.1093/ejo/22.5.499 google scholar
  • Hwang, J.-J., Jung, Y.-H., Cho, B.-H., & Heo, M.-S. (2019). An overview of deep learning in the field of dentistry. Imaging Science in Dentistry, 49(1), 1. https://doi.org/10.5624/isd.2019.49.1.1 google scholar
  • Imangaliyev, S., van der Veen, M. H., Volgenant, C. M. C., Keijser, B. J. F., Crielaard, W., & Levin, E. (2016). Deep Learning for Classification of Dental Plaque Images. https://doi.org/10.1007/978-3-319-51469-7_34 google scholar
  • Jeffcoat, M. K. (1994). Current concepts in periodontal disease testing. The Journal of the American Dental Association, 125(8), 1071-1078. https://doi.org/10.14219/jada.archive.1994.0136 google scholar
  • Jeong, S. H., Yun, J. P., Yeom, H.-G., Lim, H. J., Lee, J., & Kim, B. C. (2020). Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs. Scientific Reports, 10(1), 16235. https://doi.org/10.1038/s41598-020-73287-7 google scholar
  • Kats, L., Vered, M., Zlotogorski-Hurvitz, A., & Harpaz, I. (2019). Atherosclerotic carotid plaque on panoramic radiographs: neural network detection. International Journal of Computerized Dentistry, 22(2), 163-169. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/31134222 google scholar
  • Kawaguchi, K., Kaelbling, L. P., & Bengio, Y. (2017). Generalization in Deep Learning. Retrieved from http:// arxiv.org/abs/1710.05468 google scholar
  • Kazandjian, S., Kiliaridis, S., & Mavropoulos, A. (2006). Validity and reliability of a new edge-based computerized method for identification of cephalometric landmarks. The Angle Orthodontist, 76(4), 619-624. https:// doi.org/10.1043/0003-3219(2006)076[0619:VAROAN]2.0.CO;2 google scholar
  • Kim, J., Lee, H.-S., Song, I.-S., & Jung, K.-H. (2019). DeNTNet: Deep Neural Transfer Network for the detection of periodontal bone loss using panoramic dental radiographs. Scientific Reports, 9(1), 17615. https://doi. org/10.1038/s41598-019-53758-2 google scholar
  • Koivisto, T., Bowles, W. R., & Rohrer, M. (2012). Frequency and Distribution of Radiolucent Jaw Lesions: A Retrospective Analysis of 9,723 Cases. Journal of Endodontics, 38(6), 729-732. https://doi.org/10.1016/j. joen.2012.02.028 google scholar
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386 google scholar
  • Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., ... Schwendicke, F. (2019). Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Scientific Reports, 9(1), 8495. https:// doi.org/10.1038/s41598-019-44839-3 google scholar
  • Krois, J., Garcia Cantu, A., Chaurasia, A., Patil, R., Chaudhari, P. K., Gaudin, R., . Schwendicke, F. (2021). Generalizability of deep learning models for dental image analysis. Scientific Reports, 11(1), 1-7. https:// doi.org/10.1038/s41598-021-85454-5 google scholar
  • Langen, H.-J., Fuhrmann, R., Diedrich, P., & Günther, R. W. (1995). Diagnosis of Infra-Alveolar Bony Lesions in the Dentate Alveolar Process with High-Resolution Computed Tomography. Investigative Radiology, 30(7), 421-426. https://doi.org/10.1097/00004424-199507000-00005 google scholar
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/ nature14539 google scholar
  • LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010). Convolutional networks and applications in vision. Proceedings of 2010 IEEE International Symposium on Circuits and Systems, 253-256. https://doi.org/10.1109/ ISCAS.2010.5537907 google scholar
  • Lee, C.-T., Kabir, T., Nelson, J., Sheng, S., Meng, H.-W., Van Dyke, T. E., . Shams, S. (2021). Use of the Deep Learning Approach to Measure Alveolar Bone Level. Retrieved from http://arxiv.org/abs/2109.12115 google scholar
  • Lee, E.-S., Kang, S.-M., Ko, H.-Y., Kwon, H.-K., & Kim, B.-I. (2013). Association between the cariogenicity of a dental microcosm biofilm and its red fluorescence detected by Quantitative Light-induced Fluorescence-Digital (QLF-D). Journal ofDentistry, 41(12), 1264-1270. https://doi.org/10.1016/j.jdent.2013.08.021 google scholar
  • Lee, J.-H., Kim, D.-H., Jeong, S.-N., & Choi, S.-H. (2018). Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry, 77, 106-111. https://doi. org/10.1016/j.jdent.2018.07.015 google scholar
  • Lee, J.-S., Adhikari, S., Liu, L., Jeong, H.-G., Kim, H., & Yoon, S.-J. (2019). Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofacial Radiology, 48(1), 20170344. https://doi.org/10.1259/dmfr.20170344 google scholar
  • Lee, K.-S., Ryu, J.-J., Jang, H. S., Lee, D.-Y., & Jung, S.-K. (2020). Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Applied Sciences, 10(6), 2124. https://doi.org/10.3390/app10062124 google scholar
  • Lee, S., Oh, S., Jo, J., Kang, S., Shin, Y., & Park, J. (2021). Deep learning for early dental caries detection in bitewing radiographs. Scientific Reports, 11(1), 16807. https://doi.org/10.1038/s41598-021-96368-7 google scholar
  • Li, W., Liang, Y., Zhang, X., Liu, C., He, L., Miao, L., & Sun, W. (2021). A deep learning approach to automatic gingivitis screening based on classification and localization in RGB photos. Scientific Reports, 11(1), 1-15. https://doi.org/10.1038/s41598-021-96091-3 google scholar
  • Lian, L. (2021). Deep Learning for Caries Detection and Classification. google scholar
  • Lin, P. L., Lai, Y. H., & Huang, P. W. (2010). An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4), 1380-1392. https://doi.org/10.1016/j.patcog.2009.10.005 google scholar
  • Lindner, C., Wang, C.-W., Huang, C.-T., Li, C.-H., Chang, S.-W., & Cootes, T. F. (2016). Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms. Scientific Reports, 6(1), 33581. https://doi.org/10.1038/srep33581 google scholar
  • Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. O. (2019). Questions for Artificial Intelligence in Health Care. JAMA, 321(1), 31-32. https://doi.org/10.1001/jama.2018.18932 google scholar
  • Miki, Y., Muramatsu, C., Hayashi, T., Zhou, X., Hara, T., Katsumata, A., & Fujita, H. (2017). Classification of teeth in cone-beam CT using deep convolutional neural network. Computers in Biology and Medicine, 80, 24-29. https://doi.org/10.1016/j.compbiomed.2016.11.003 google scholar
  • Moriyama, Y., Lee, C., Date, S., Kashiwagi, Y., Narukawa, Y., Nozaki, K., & Murakami, S. (2019a). A MapReduce-like Deep Learning Model for the Depth Estimation of Periodontal Pockets. Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, 388-395. https://doi. org/10.5220/0007405703880395 google scholar
  • Moriyama, Y., Lee, C., Date, S., Kashiwagi, Y., Narukawa, Y., Nozaki, K., & Murakami, S. (2019b). Evaluation of Dental Image Augmentation for the Severity Assessment of Periodontal Disease. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 924-929. https://doi. org/10.1109/CSCI49370.2019.00176 google scholar
  • Moutselos, K., Berdouses, E., Oulis, C., Maglogiannis, I., & Member, S. (2019). 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), 1617-1620. google scholar
  • Mupparapu, M., Wu, C.-W., & Chen, Y.-C. (2018). Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis. Quintessence International (Berlin, Germany : 1985), 49(9), 687-688. https://doi.org/10.3290/j.qi.a41107 google scholar
  • Murata, M., Ariji, Y., Ohashi, Y, Kawai, T., Fukuda, M., Funakoshi, T., .. Ariji, E. (2019). Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiology, 35(3), 301-307. https://doi.org/10.1007/s11282-018-0363-7 google scholar
  • Nakano, Y., Suzuki, N., & Kuwata, F. (2018). Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach. BMC Oral Health, 18(1), 128. https://doi.org/10.1186/s12903-018-0591-6 google scholar
  • Orhan, K., Bayrakdar, I. S., Ezhov, M., Kravtsov, A., & Ozyurek, T. (2020). Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. International Endodontic Journal, 53(5), 680-689. https://doi.org/10.1111/iej.13265 google scholar
  • Park, J., Hwang, H., Moon, J., Yu, Y., Kim, H., & Her, S. (2019). Automated identification of cephalometric landmarks : Part 1 — Comparisons between the latest deep-learning methods YOLOV3 and SSD. 89(6), 8-11. https://doi.org/10.2319/022019-127.1 google scholar
  • Park, W. J., & Park, J.-B. (2018). History and application of artificial neural networks in dentistry. European Journal of Dentistry, 12(04), 594-601. https://doi.org/10.4103/ejd.ejd_325_18 google scholar
  • Pauwels, R., Brasil, D. M., Yamasaki, M. C., Jacobs, R., Bosmans, H., Freitas, D. Q., & Haiter-Neto, F. (2021). Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 131(5), 610-616. https://doi.org/10.1016/j.oooo.2021.01.018 google scholar
  • Pihlstrom, B. L., Michalowicz, B. S., & Johnson, N. W. (2005). Periodontal diseases. The Lancet, 366(9499), 1809-1820. https://doi.org/10.1016/S0140-6736(05)67728-8 google scholar
  • Rabunal, J. R., & Dorado, J. (Eds.). (2006). Artificial Neural Networks in Real-Life Applications. https://doi. org/10.4018/978-1-59140-902-1 google scholar
  • Rana, A., Yauney, G., Wong, L. C., Gupta, O., Muftu, A., & Shah, P. (2017). Automated segmentation of gingival diseases from oral images. 2017 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), 144-147. https://doi.org/10.1109/HIC.2017.8227605 google scholar
  • Reddy, M. S. (1992). Radiographic Methods in the Evaluation of Periodontal Therapy. Journal of Periodontology, 63(12s), 1078-1084. https://doi.org/10.1902/jop.1992.63.12s.1078 google scholar
  • Rudolph, D. J., Sinclair, P. M., & Coggins, J. M. (1998). Automatic computerized radiographic identification of cephalometric landmarks. American Journal of Orthodontics and Dentofacial Orthopedics, 113(2), 173-179. https://doi.org/10.1016/S0889-5406(98)70289-6 google scholar
  • Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial Intelligence in Dentistry: Chances and Challenges. Journal of Dental Research, 99(7), 769-774. https://doi.org/10.1177/0022034520915714 google scholar
  • Schwendicke, Falk, Golla, T., Dreher, M., & Krois, J. (2019). Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry, 91, 103226. https://doi.org/10.1016/j.jdent.2019.103226 google scholar
  • Shin, W., Yeom, H.-G., Lee, G. H., Yun, J. P., Jeong, S. H., Lee, J. H., ... Kim, B. C. (2021). Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health, 21(1), 130. https://doi.org/10.1186/s12903-021-01513-3 google scholar
  • Suh, H., Lee, H., Lee, Y., Eo, S., Donatelli, R. E., & Lee, S. (n.d.). Predicting soft tissue changes after orthognathic surgery : The sparse partial least squares method. https://doi.org/10.2319/120518-851.1 google scholar
  • Sukegawa, S., Yoshii, K., Hara, T., Yamashita, K., Nakano, K., Yamamoto, N., . Furuki, Y. (2020). Deep Neural Networks for Dental Implant System Classification. Biomolecules, 10(7). https://doi.org/10.3390/ biom10070984 google scholar
  • Takahashi, T., Nozaki, K., Gonda, T., & Ikebe, K. (2021). A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network. Journal of Prosthodontic Research, 65(1), 115-118. https://doi.org/10.2186/jpr.JPOR_2019_354 google scholar
  • Takahashi, T., Nozaki, K., Gonda, T., Mameno, T., & Ikebe, K. (2021). Deep learning-based detection of dental prostheses and restorations. Scientific Reports, 11(1), 1-7. https://doi.org/10.1038/s41598-021-81202-x google scholar
  • Takahashi, T., Nozaki, K., Gonda, T., Mameno, T., Wada, M., & Ikebe, K. (2020). Identification of dental implants using deep learning—pilot study. International Journal ofImplant Dentistry, 6(1), 53. https://doi. org/10.1186/s40729-020-00250-6 google scholar
  • Thanathornwong, B., & Suebnukarn, S. (2020). Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Science in Dentistry, 50(2), 169. https://doi.org/10.5624/isd.2020.50.2.169 google scholar
  • Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7 google scholar
  • Tuzoff, D. V., Tuzova, L. N., Bornstein, M. M., Krasnov, A. S., Kharchenko, M. A., Nikolenko, S. I., . Bed-nenko, G. B. (2019). Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofacial Radiology, 48(4), 1-10. https://doi.org/10.1259/dmfr.20180051 google scholar
  • Uthoff, R. D., Song, B., Sunny, S., Patrick, S., Suresh, A., Kolur, T., . Liang, R. (2018). Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. PLOS ONE, 13(12), e0207493. https://doi.org/10.1371/journal.pone.0207493 google scholar
  • van der Veen, M.H., Thomas, R. Z., Huysmans, M. C. D. N. J. M., & de Soet, J. J. (2006). Red Autofluorescence of Dental Plaque Bacteria. Caries Research, 40(6), 542-545. https://doi.org/10.1159/000095655 google scholar
  • van der Veen, Monique H., Volgenant, C. M. C., Keijser, B., ten Cate, J. (Bob) M., & Crielaard, W. (2016). Dynamics of red fluorescent dental plaque during experimental gingivitis—A cohort study. Journal ofDentistry, 48, 71-76. https://doi.org/10.1016/j.jdent.2016.02.010 google scholar
  • Vinayahalingam, S., Xi, T., Berge, S., Maal, T., & de Jong, G. (2019). Automated detection of third molars and mandibular nerve by deep learning. Scientific Reports, 9(1), 9007. https://doi.org/10.1038/s41598-019-45487-3 google scholar
  • Xu, X., Liu, C., & Zheng, Y. (2019). 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. IEEE Transactions on Visualization and Computer Graphics, 25(7), 2336-2348. https://doi. org/10.1109/TVCG.2018.2839685 google scholar
  • You, W., Hao, A., Li, S., Wang, Y., & Xia, B. (2020). Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. 1-7. https://doi.org/10.21203/rs.2.21027/v1 google scholar
  • Yu, M., Yan, H., Xia, J., Zhu, L., Zhang, T., Zhu, Z., . Dong, M. (2019). Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy. Photodiagnosis and Photodynamic Therapy, 26, 430-435. https://doi.org/10.1016/j.pdpdt.2019.05.008 google scholar
  • Zhang, K., Wu, J., Chen, H., & Lyu, P. (2018). An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics, 68, 61-70. https://doi.org/10.1016/j. compmedimag.2018.07.001 google scholar


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.