Abstract :
Artificial intelligence in the medical field has gained an immense boost after successful trials in various fields. Artificial intelligence is computer science that is able to replicate human behavior. After its use in medical science, its use has been done in dentistry also. Artificial intelligence is adding new dimensions to dentistry. It has been used in the diagnosis of vital structures, anatomy, etc. This article reviewed the uses of artificial intelligence in the field of oral diagnosis.
Keywords :
Anatomy, Oral diagnosis, Vital.References :
- Barr A, Feigenbaum EA. The Handbook of Artificial Intelligence. William Kaufmann. Inc., Los Altos, CA. 1981;1.F. Schwendicke, W. Samek, and J. Krois, “Artificial intelligence in dentistry: chances and challenges,” Journal of Dental Research, vol. 99, no. 7, pp. 769–774, 2020.
- Bose S, Sur J, Khan F, Dewangan D, Tuzoff D, Raza MT, Roul A, Sawriya E. International Journal of Medical and Health Sciences.
- Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. European journal of radiology. 2018 Aug 1;105:246-50.
- Krupinski EA. The future of image perception in radiology: synergy between humans and computers. Academic radiology. 2003 Jan 1;10(1):1-3.
- Bose S, Sur J, Khan F, Dewangan D, Tuzoff D, Raza MT, Roul A, Sawriya E. International Journal of Medical and Health Sciences.
- Ding S, Zhao H, Zhang Y, Xu X, Nie R. Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review. 2015 Jun;44(1):103-15.
- Kahn Jr CE. Artificial intelligence in radiology: decision support systems. Radiographics. 1994 Jul;14(4):849-61.
- Mudrak J. Artificial Intelligence and Deep Learning In Dental Radiology: A way forward in point of care radiology. Oral Health [Internet]. 2019.
- 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 May;48(4):20180051.
- 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. Scientific reports. 2019 Mar 7;9(1):1-1.
- Nagi R, Aravinda K, Rakesh N, Gupta R, Pal A, Mann AK. Clinical applications and performance of intelligent systems in dental and maxillofacial radiology: A review. Imaging Science in Dentistry. 2020 Jun;50(2):81.
- Sharma D, Kumar N. A review on machine learning algorithms, tasks and applications. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). 2017 Oct;6(10):2278-1323.
- 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 Oct 1;77:106-11.
- Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, Dörfer C, Schwendicke F. Deep learning for the radiographic detection of periodontal bone loss. Scientific reports. 2019 Jun 11;9(1):1-6.
- Kim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Scientific reports. 2019 May 6;9(1):1-0.
- Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, Katsumata A, Ariji E. 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. 2019 May 1;127(5):458-63.
- Pakdemirli E. Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading?. Acta radiologica open. 2019 Feb;8(2):2058460119830222.
- Kwon O, Yong TH, Kang SR, Kim JE, Huh KH, Heo MS, Lee SS, Choi SC, Yi WJ. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofacial Radiology. 2020 Dec 1;49(8):20200185.
- Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, Huh KH, Lee SS, Heo MS, Choi SC, Kim TI. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Scientific reports. 2020 May 5;10(1):1-8.Hwang J-J, Jung Y-H, Cho B-H, Heo M-S. An overview of deep learning in the field of dentistry. Imaging Sci Dent 2019; 49: 1–7.
- 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. Dentomaxillofacial Radiology. 2020 Jan;49(1):20190107.
- Maret D, Telmon N, Peters OA, Lepage B, Treil J, Inglèse JM, Peyre A, Kahn JL, Sixou M. Effect of voxel size on the accuracy of 3D reconstructions with cone beam CT. Dentomaxillofacial Radiology. 2012 Dec;41(8):649-55.
- Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A, Fujita H. Classification of teeth in cone-beam CT using deep convolutional neural network. Computers in biology and medicine. 2017 Jan 1;80:24-9.
- Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics. 2007 Jun 1;31(4-5):198-211.
- Taguchi A, Tsuda M, Ohtsuka M, Kodama I, Sanada M, Nakamoto T, Inagaki K, Noguchi T, Kudo Y, Suei Y, Tanimoto K. Use of dental panoramic radiographs in identifying younger postmenopausal women with osteoporosis. Osteoporosis international. 2006 Mar;17(3):387-94.
- Khatoonabad MJ, Aghamohammadzade N, Taghilu H, Esmaeili F, Khamnei HJ. Relationship among panoramic radiography findings, biochemical markers of bone turnover and hip BMD in the diagnosis of postmenopausal osteoporosis. Iranian Journal of Radiology. 2011 Mar;8(1):23.
- Kavitha MS, An SY, An CH, Huh KH, Yi WJ, Heo MS, Lee SS, Choi SC. Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women. Oral surgery, oral medicine, oral pathology and oral radiology. 2015 Mar 1;119(3):346-56.
- Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, Park W. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofacial Radiology. 2017 Oct;46(7):20170006.
- Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, Nagatsuka H, Furuki Y. Deep neural networks for dental implant system classification. Biomolecules. 2020 Jul 1;10(7):984.
- Rosalia L, Daniela G, Francesco M, Spampinato C. Automatic cephalometric analysis a systematic review. Angle Orthod. 2008;78(145):120506-491.
- Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, Sardana HK. Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofacial Radiology. 2018 Jan;47(2):20170054.
- Ogura I, Kobayashi E, Nakahara K, Haga-Tsujimura M, Igarashi K, Katsumata A. Computer programme to assess mandibular cortex morphology in cases of medication-related osteonecrosis of the jaw with osteoporosis or bone metastases. Imaging science in dentistry. 2019 Dec 1;49(4):281-6.
- Sam A, Currie K, Oh H, Flores-Mir C, Lagravere-Vich M. Reliability of different three-dimensional cephalometric landmarks in cone-beam computed tomography: A systematic review. The Angle Orthodontist. 2019 Mar;89(2):317-32.
- Nassar DE, Ammar HH. A neural network system for matching dental radiographs. Pattern Recognition. 2007 Jan 1;40(1):65-79.
- Zhang K, Wu J, Chen H, Lyu P. An effective teeth recognition method using label tree with cascade network structure. Computerized Medical Imaging and Graphics. 2018 Sep 1;68:61-70.