ORAL VE MAKSİLLOFASİYAL RADYOLOJİ’DE YAPAY ZEKA

Teknoloji 90’lardan günümüze hızlı ilerleme kaydetti ve bu gelişmeler günlük yaşamımızda yerlerini almıştır. Son on yılda yapay zekanın (Artificial Intelligence, AI) evriminde, diş hekimliğini de kapsayan muazzam bir gelişme izlenmektedir. Pek çok gelişmeden bağımsız olarak, AI hala emekleme aşamasında olmakla birlikte potansiyeli sınırsızdır. Yapay zekanın evrimi, güvenilir bilgi sağlayan ve karar verme sürecini iyileştiren büyük verilerin analizini mümkün kılmaktadır. Göstermemiz gereken teknolojik adaptasyon ve konu ile ilgili kapsamlı bilgi sahibi olmak, sadece daha iyi ve hassas hasta bakımına yardımcı olmakla kalmayacak, aynı zamanda klinisyenin iş yükünü de azaltacaktır. AI, diş hekimliğinde özellikle hasta teşhisi, hasta verilerinin depolanması ve hastalar için gelişmiş bir sağlık hizmeti sağlamak için oral ve maksillofasiyal radyolojide önemli olup,AI, oral ve maksillofasiyal radyoloji alanına da yavaş ama istikrarlı bir şekilde nüfuz etmektedir. Bu derleme, AI yöntemlerinin genel bir analizini, özellikle oral ve maksillofasiyal radyolojide görüntü tabanlı görevlerle ilgili olanları gözden geçirmektedir.

Anahtar Kelimeler:

Derin öğrenme, diş hekimliği, oral radyoloji, yapay sinir ağları, yapay zeka.

ARTIFICIAL INTELLIGENCE IN ORAL AND MAXILLOFACIAL RADIOLOGY

Technology has progressed rapidly since the 90s and these developments took their place in our daily life. There has been a tremendous improvement and a marked increase in the evolution of artificial intelligence (AI) over the past decade, including dentistry. Regardless of many developments, AI is still in its infancy, but its potential is unlimited. The evolution of artificial intelligence enables the analysis of big data that provides reliable information and improves decision making. The technological adaptation we need to demonstrate and a thorough knowledge of the subject will not only help better and more precise patient care, but also reduce the clinician's workload. AI is important in dentistry, especially in oral and maxillofacial radiology for patient diagnosis, storage of patient data, and providing an improved healthcare service for patients. AI is also slowly but steadily penetrating the field of oral and maxillofacial radiology. This review revises a general analysis of AI methods, particularly those related to image-based tasks in oral and maxillofacial radiology.

Keywords:

Artificial intelligence (AI), artificial neural networks, deep learning, dentistry, oral radiology.,

PDF

___

McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence. AI Magazine 2006; 27:12-12.
Alexander B, John S, Aralamoodu PO. Artificial intelligence in dentistry: current concepts and a peep into the future. International Journal of Advanced Research 2018; 6:1105-1108.
Thrall JH, Li X, Li Q, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Jounal of the American College of Radiology 2018; 15:504-508.
Wong SH, Al-Hasani H, Alam Z, Alam A. Artificial intelligence in radiology: how will we be affected? Eur J Radiol 2019; 29:141-143.
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg 2018; 268:70-76.
Deshmukh S. Artificial intelligence in dentistry. International Clinical Dental Research Organisation 2018; 10:47-48.
Park WJ, Park J-B. History and application of artificial neural networks in dentistry. European Journal of Dentistry 2018; 12:594-601.
Aslan E. Yabancı dil öğretiminde robot öğretmenle. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi 2014; 33:15-26.
Shortliffe EH. Computer programs to support clinical decision making. JAMA 1987; 258:61-66.
Ishak WHW, Siraj F. Artificial intelligence in medical application: An exploration. Health İnformatics Europe Journal 2002; 16.
Best ML, Wade KW. The Internet and democracy: global catalyst or democratic dud? Bulletin of Science, Technology & Society 2009; 29:255-271.
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2017; 2:230-243.
Salari N, Shohaimi S, Najafi F, Nallappan M, Karishnarajah I. A novel hybrid classification model of genetic algorithms, modified k-nearest neighbor and developed backpropagation neural network. PLOS ONE 2014; 9:e112987.
Hwang JJ, Azernikov S, Efros AA, Yu SX. Learning beyond human expertise with generative models for dental restorations. 2018.
Khanna DSS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian Journal of Basic and Applied Medical Research. 2017; 6:161-167
Goodfellow I, Bengio Y, Courville A. Deep learning. Healthcare Informatic Research 2016; 22:351-354.
Bahnsen AC. Building AI applications using deep learning. 2016.
Tang A, Tam R, Cadrin-Chênevert A, et al. Canadian association of radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018; 69:120-135.
Mol A, van der Stelt PF. Application of digital image analysis in dental radiography for the description of periapical bone lesions: a preliminary study. IEEE Trans Biomed Eng. 1991; 38:357-359.
Saghiri MA, et al. A new approach for locating the minor apical foramen using an artificial neural network. International Endodontic Journal 2012; 45:257–265.
Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. Journal of Endodontics 2012; 38:1130-1134.
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.
Devito KL, de Souza Barbosa F, Filho WNF. 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.
Flores A, Rysavy S, Enciso R, Okada K. Non-invasive differential diagnosis of dental periapical lesions in cone-beam CT. IEEE international symposium on biomedical imaging: From nano to macro 2009; pp 566-569.
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; 49:20190107.
Montúfar J, Romero M, Scougall-Vilchis RJ. Hybrid approach for automatic cephalometric landmark annotation on cone-beam computed tomography volumes. Am J Orthod Dentofacial Orthop 2018; 154:140-150.
Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis: a systemetic review. The Angle Orthodontist 2008; 78:145-151.
Aghaloo T, Pi-Anfruns J, Moshaverinia A, et al. The effects of systemic diseases and medications on implant osseointegration: a systematic review. Int J Oral Maxillofac Implants 2019; 34:35–49.
Medeiros FCFL de, Kudo G a. H, Leme BG, et al. Dental implants in patients with osteoporosis: a systematic review with meta-analysis. Int J Oral Maxillofac Surg 2018; 47:480-491.
Vlasiadis KZ, Damilakis J, Velegrakis GA, et al . Relationship between BMD, dental panoramic radiographic findings and biochemical markers of bone turnover in diagnosis of osteoporosis. Maturitas 2008;59:226-233.
Taguchi A, Tsuda M, Ohtsuka M, et al. Use of dental panoramic radiographs in identifying younger postmenopausal women with osteoporosis. Osteoporos Int 2006; 17:387-394.
Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automatic segmentation of maxillofacial cysts in cone beam CT images. Comput Biol Med 2016; 72:108-119.
Rana M, Modrow D, Keuchel J, et al. Development and evaluation of an automatic tumor segmentation tool: a comparison between automatic, semi-automatic and manual segmentation of mandibular odontogenic cysts and tumors. J Craniomaxillofac Surg 2015; 43:355-359.
Mikulka J, Gescheidtová E, Kabrda M. Classification of jaw bone cysts and necrosis via the processing of orthopantomograms. Radioengineering 2013; 22:114-122.
Nurtanio I, Astuti ER, Purnama IKE, Hariadi M, Purnomo MH. Classifying cyst and tumor lesion using support vector machine based on dental panoramic images texture features. International Journal of Computer Science 2013; 40:29-37.
Yilmaz E, Kayikcioglu T, Kayipmaz S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Programs Biomed 2017; 146:91-100.
Abdolali F, Zoroofi RA, Otake Y, Sato Y. Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics. Comput Methods Programs Biomed 2017; 139:197-207.
Lin PL, Huang PW, Huang PY, Hsu HC. Alveolar bone-loss area localization in periodontitis radiographs based on threshold segmentation with a hybrid feature fused of intensity and the H-value of fractional Brownian motion model. Comput Methods Programs Biomed 2015; 121:117-126.
Lin PL, Huang PY, Huang PW. Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Comput Methods Programs Biomed 2017; 148:1-11.
Lee JH, Kim D, Jeong SN, Choi SH. 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.
Mol A, van der Stelt PF. Application of computer-aided image interpretation to the diagnosis of periapical bone lesions. Dentomaxillofacial Radiology 1992; 21:190-194.
Carmody DP, McGrath SP, Dunn SM, van der Stelt PF, Schouten E. Machine classification of dental images with visual search. Acad Radiol 2001; 8:1239-1246.
Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018; 77:106-111.
Gakenheimer DC. The efficacy of a computerized caries detector in intraoral digital radiography. JADA 2002; 133:883-890.
Wenzel A, Hintze H, Kold LM, Kold S. Accuracy of computer-automated caries detection in digital radiographs compared with human observers. Eur J Oral Sci 2002; 110:199-203.
Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial ıntelligence technology for medical diagnosis and prediction. Radiology 2018; 286:800-809.
Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med 2018; 131:129-133.
Jayalekshmy R, Unnithan JJ, Kumar AM, Majid SA. Artificial intelligence-finding new frontiers in oral and maxillofacial radiology. Journal of Dental & Oro-facial Research 2020; 16:7-7.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical Image Analysis 2017; 42:60-88