LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,187
تعداد مقالات10,824
تعداد مشاهده مقاله72,833,593
تعداد دریافت فایل اصل مقاله12,911,451
Ghods, Kimia, Azizi, Arash, Jafari, Aryan, Ghods, Kian. (1402). Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. سامانه مدیریت نشریات علمی, 24(4), 356-371. doi: 10.30476/dentjods.2023.96835.1969
Kimia Ghods; Arash Azizi; Aryan Jafari; Kian Ghods. "Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature". سامانه مدیریت نشریات علمی, 24, 4, 1402, 356-371. doi: 10.30476/dentjods.2023.96835.1969
Ghods, Kimia, Azizi, Arash, Jafari, Aryan, Ghods, Kian. (1402). 'Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature', سامانه مدیریت نشریات علمی, 24(4), pp. 356-371. doi: 10.30476/dentjods.2023.96835.1969
Ghods, Kimia, Azizi, Arash, Jafari, Aryan, Ghods, Kian. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. سامانه مدیریت نشریات علمی, 1402; 24(4): 356-371. doi: 10.30476/dentjods.2023.96835.1969

Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature

Journal of Dentistry
مقاله 1، دوره 24، شماره 4 - شماره پیاپی 81، اسفند 2023، صفحه 356-371    اصل مقاله (502.66 K)
نوع مقاله: Literature Review
شناسه دیجیتال (DOI): 10.30476/dentjods.2023.96835.1969
نویسندگان
Kimia Ghods* 1؛ Arash Azizi2؛ Aryan Jafari3؛ Kian Ghods4
1Student of Dentistry, Membership of Dental Material Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
2Dept. Oral Medicine, Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
3Student of Dentistry, Membership of Dental Material Research Center, Tehran.
4Dept. of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada.
چکیده
Statement of the Problem: In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation.
Purpose: Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages.
Materials and Method: For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence”, "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated.
Results: In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed.
Conclusion: As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
کلیدواژه‌ها
Artificial Intelligence؛ Dentistry؛ Machine learning؛ Deep learning؛ Diagnostic System
سایر فایل های مرتبط با مقاله
مراجع
  • Alexander B, John S. Artificial Intelligence in Dentistry: Current Concepts and a Peep into the Future. Int J Adv Res. 2018; 6:1105–8.
  • Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res. 2020; 99: 769-774.
  • Miladinović M, Mihailović B, Mladenović D, Duka M, Živković D, Mladenović S, et al. Artificial intelligence in clinical medicine and dentistry. Vojnosanit Pregl. 2017; 74: 267–272.
  • Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J Arthroplasty. 2018; 33: 2358–2361.
  • Kassianos AP, Emery JD, Murchie P, Walter FM. Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review. Br J Dermatol. 2015, 172: 1507–1518.
  • Meghil MM, Rajpurohit P, Awad ME, McKee J, Shahoumi LA, Ghaly M. Artificial intelligence in dentistry. Dent Rev. 2022; 2: 10009.
  • Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018; 6: 75.
  • Hwang JJ, Jung YH, Cho BH, Heo MS. An overview of deep learning in the field of dentistry. Imaging Sci Dent. 2019; 49: 1–7.
  • Sharma S. Artificial intelligence in dentistry: the current concepts and a peek into the future. Int J Contemp Med Res. 2019; 6: L5-L9.
  • Khanna S, Dhaimade P. Artificial Intelligence: Transforming Dentistry Today. Indian J Basic Appl Med Res. 2017; 6: 161-167.
  • McCall COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health. 2020; 2: e166–e167.
  • Khanagar SB, Al-ehaideb A, Maganur PC, Vishwanath-aiah S, Patil S, Baeshen HA, et al. Developments, application, and performance of artificial intelligence in dentistry: A systematic review. J Dent Sci. 2021; 16: 508–522.
  • Machoy ME, Szyszka-Sommerfeld L, Vegh A, Gedrange T, Woźniak K. The ways of using machine learning in dentistry. Adv Clin Exp Med: Official Organ Wroclaw Medical University. 2020; 29: 375-384.
  • Kishimoto T, Goto T, Matsuda T, Iwawaki Y, Ichikawa T. Application of artificial intelligence in the dental field: A literature review. J Prosthodont Res. 2022; 66: 19-28.
  • Pethani F. Promises and Perils of Artificial Intelligence in Dentistry. Australian Dent J. 2021; 66: 124-135.
  • Reyes LT, Knorst JK, Ortiz FR, Ardenghi TM. Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review. J Clin Transl Res. 2021; 7: 523-539.
  • Heo M, Kim J, Hwang J, Han S, Kim J, Yi W, et al. Artificial intelligence in oral and maxillofacial radiology: what is currently possible? Dentomaxillofac Radiol. 2021; 50: 20200375.
  • Kim M, Yun J, Cho Y, Shin K, Jang R, Bae HJ, et al. Deep learning in medical imaging. Neurospine. 2019; 16: 657-668.
  • Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2018; 6: 9375–9389.
  • Hesamian MH, Jia W, He X, Kennedy P. Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging. 2019; 32: 582–596.
  • Do S, Song KD, Chung JW. Basics of deep learning: a radiologist's guide to understanding published radiology articles on deep learning. Korean J Radiol. 2020; 21: 33-41.
  • Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, et al. Deep learning workflow in radiology: a primer. Insights Imaging. 2020; 11: 22.
  • 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.
  • Thanathornwong B, Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Sci Dent. 2020; 50: 169-174.
  • Fukuda M, Inamoto K, Shibata N. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020; 36: 337–343.
  • Orhan K, Bayrakdar IS, Ezhov M, Kravtsov A, Ozyürek T. Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans. Int Endod J. 2020; 53: 680–689.
  • Chang HJ, Lee SJ, Yong TH, Shin NY, Jang BG, Kim JE, et al. Deep learning hybrid method to automatically diagnose periodontal bone loss and stage periodontitis. Sci Rep. 2020; 10: 7531.
  • Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019; 48: 20180051.
  • De Tobel J, Radesh P, Vandermeulen D, Thevissen PW. An automated technique to stage lower third molar development on panoramic radiographs for age estimation: a pilot study. J Forensic Odontostomatol. 2017; 35: 42-54. 
  • Hwang JJ, Lee JH, Han SS, Kim YH, Jeong HG, Choi YJ, et al. Strut analysis for osteoporosis detection model using dental panoramic radiography. Dentomaxillofac Radiol. 2017; 46: 20170006.
  • Lee JS, Adhikari S, Liu L, Jeong HG, Kim H, Yoon SJ. Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study. Dentomaxillofac Radiol. 2019; 48: 20170344.
  • Kim JR, Shim WH, Yoon HM, Hong SH, Lee JS, Cho YA, et al. Computerized bone age estimation using deep learning-based program: evaluation of the accuracy and efficiency. AJR Am J Roentgenol. 2017; 209: 1374–1380.
  • Vernucci RA, Aghazada H, Gardini K, Fegatelli DA, Barbato E, Galluccio G, et al. Use of an anatomical mid-sagittal plane for 3-dimensional cephalometry: a preliminary study. Imaging Sci Dent. 2019; 49: 159–169.
  • Neelapu BC, Kharbanda OP, Sardana V, Gupta A, Vasamsetti S, Balachandran R, et al. Automatic localization of three-dimensional cephalometric landmarks on CBCT images by extracting symmetry features of the skull. Dentomaxillofac Radiol. 2018; 47: 20170054.
  • Zhang K, Zuo W, Chen Y, Meng D, Zhang L. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process. 2017; 26: 3142–3155.
  • Zhang Y, Yu H. Convolutional neural network-based metal artifact reduction in X-ray computed tomography. IEEE Trans Med Imaging. 2018; 37: 1370–1381.
  • Yaji A, Prasad S, Pai A. Artificial intelligence in dento-maxillofacial radiology. Acta Sci Dent Sci. 2019; 3: 116-121.
  • Sachdeva S, Mani A, Vora H, Saluja H, Mani S, Manka N. Artificial Intelligence in Periodontics: A Dip in the Future. J Cellular Biotech. 2021; 7: 119-124.
  • Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of Artificial Intelligence in Dentistry: Current Clinical Trends and Research Advances. J Can Dent Assoc. 2021; 87: 17.
  • Reddy MS, Shetty SR, Shetty RM, Vannala V, Sk S. Future of periodontics lies in artificial intelligence: Myth or reality? J Investig Clin Dent. 2019; 10: e12423.
  • Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018; 48: 114-123.
  • Yauney G, Rana A, Wong LC, Javia P, Muftu A, Shah P. Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health. Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019: 3387-3393.
  • Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One. 2014; 9: e89757.
  • Revilla-León M, Gómez-Polo M, Barmak A, Inam W, Kan J, Kois J, et al. Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J Prosthet Dent. 2022; S0022-3913(22)00075-0.
  • Revilla-León M, Gómez-Polo M, Vyas S. Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent. 2021; S0022-3913(21)00272-9.
  • Roy S, Dey S, Khutia N, Roy-Chowdhury A, Datta S. Design of patient specific dental implant using FE analysis and computational intelligence techniques. Appl Soft Comput. 2018; 65: 272-279.
  • Zhang H, Shan J, Zhang P, Chen X, Jiang H. Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible. Sci Rep. 2020; 10: 18437.
  • Ha SR, Park HS, Kim EH. A pilot study using machine learning methods about factors influencing prognosis of dental implants. J Adv Prosthodont. 2018; 10: 395-400.
  • Liu CH, Lin CJ, Hu YH, You ZH. Predicting the Failure of Dental Implants Using Supervised Learning Techniques. Applied Sciences. 2018; 8: 698.
  • Lee CT, Kabir T, Nelson J. Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol. 2022; 49: 260-269.
  • Sukegawa S, Yoshii K, Hara T. Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images. Biomolecules. 2021; 11: 815.
  • Mahmood H, Shaban M, Indave BI, Santos-Silva AR, Rajpoot N, Khurram SA. Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review. Oral Oncol. 2020; 110: 104885.
  • Baik J, Ye Q, Zhang L, Poh C, Rosin M, MacAulay C, et al. Automated classification of oral premalignant lesions using image cytometry and random forests-based algorithms. Cellular Oncol. 2014; 37: 193–202.
  • Krishnan MM, Venkatraghavan V, Acharya UR, Pal M, Paul RR, Min LC, et al. Automated oral cancer identification using histopathological images: a hybrid feature extraction paradigm. Micron. 2012; 43: 352–364.
  • Krishnan MM, Shah P, Chakraborty C, Ray AK. Statistical analysis of textural features for improved classification of oral histopathological images. J Med Syst. 2012; 36: 865–881.
  • Krishnan MM, Pal M, Bomminayuni SK, Chakraborty C, Paul RR, Chatterjee J, et al. Automated classification of cells in sub-epithelial connective tissue of oral submucous fibrosis: An SVM based approach. Comput Biol Med. 2009; 39: 1096–1104.
  • Krishnan MM, Choudhary A, Chakraborty C, Ray AK, Paul RR. Texture based segmentation of epithelial layer from oral histological images. Micron. 2011; 42: 632–641.
  • Mookiah MR, Shah P, Chakraborty C, Ray AK. Brownian motion curve-based textural classification and its application in cancer diagnosis. Anal Quant Cytol Histol. 2011; 33: 158–168.
  • Das DK, Bose S, Maiti AK, Mitra B, Mukherjee G, Dutta PK. Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis. Tissue Cell. 2018; 1: 111–119.
  • Rahman TY, Mahanta LB, Chakraborty C, Das AK, Sarma JD. Textural pattern classification for oral squamous cell carcinoma. J Microsc. 2018; 269: 85–93.
  • Sun YN, Wang YY, Chang SC, Wu LW, Tsai ST. Color-based tumor tissue segmentation for the automated estimation of oral cancer parameters. Microsc Res Tech. 2010; 73: 5–13.
  • Lorsakul A, Andersson E, Harring SV, Sade H, Grimm O, Bredno J. Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts. InMedical Imaging 2017: Digital Pathology 2017; 10140: 41-46.
  • Fouad S, Randell D, Galton A, Mehanna H, Landini G. Unsupervised morphological segmentation of tissue compartments in histopathological images. PLoS One. 2017; 12: e0188717. 
  • Mahmood H, Shaban M, Rajpoot N. Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer. 2021; 124:1934–1940.
  • Yang J, Xie Y, Liu L, Xia B, Cao Z, Guo C. Automated dental image analysis by deep learning on small dataset. In2018 IEEE 42nd annual computer software and applications conference. COMPSAC. 2018; 1: 492-497.
  • Bernauer SA, Zitzmann NU, Joda T. The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review. Sensors (Basel). 2021; 21: 6628.
  • Zhang B, Dai N, Tian S, Yuan F, Yu Q. The extraction method of tooth preparation margin line based on soctree cnn. Int J Numer Method Biomed Engineer. 2019; 35: 3241.
  • Lerner H, Mouhyi J, Admakin O, Mangano F. Artificial intelligence in fixed implant prosthodontics: a retrospective study of 106 implant-supported monolithic zirconia crowns inserted in the posterior jaws of 90 patients. BMC Oral Health. 2020; 20: 80.
  • Yamaguchi S, Lee C, Karaer O, Ban S, Mine A, Imazato S. Predicting the Debonding of CAD/CAM Composite Resin Crowns with AI. J Dent Res. 2019; 98: 1234-1238.
  • Wei J, Peng M, Li Q, Wang Y. Evaluation of a Novel Computer Color Matching System Based on the Improved Back-Propagation Neural Network Model. J Prosthodont. 2018; 27: 775-783.
  • Takahashi T, Nozaki K, Gonda T, Ikebe K. A system for designing removable partial dentures using artificial intelligence. Part 1. Classification of partially edentulous arches using a convolutional neural network. J Prosthodont Res. 2021; 65: 115-118.
  • Pareek M, Kaushik B. Artificial intelligence in prosthodontics: a scoping review on current applications and future possibilities. Int J Adv Med. 2022; 9: 367-370.
  • Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry: Narrative Review. Int J Enviro Res Pub Health. 2022; 19: 3449.
  • Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental caries diagnosis and detection using neural networks: A systematic review. J Clin Med. 2020; 9: 3579.
  • Xiao J, Luo J, Ly-Mapes O, Wu TT, Dye T, Al Jallad N, et al. Assessing a smartphone app (AICaries) that uses artificial intelligence to detect dental caries in children and provides interactive oral health education: Protocol for a design and usability testing study. JMIR Res Protoc. 2021; 10: e32921.
  • Geetha V, Aprameya KS, Hinduja DM. Dental caries diagnosis in digital radiographs using back-propagation neural network. Health Inf Sci Syst. 2020; 8: 1–14.
  • Abdalla-Aslan R, Yeshua T, Kabla D, Leichter I, Nadler C. An artificial intelligence system using machine-learning for automatic detection and classification of dental restorations in panoramic radiography. Oral Surg. Oral Med. Oral Pathol Oral Radiol. 2020; 130: 593–602.
  • Javed S, Zakirulla M, Baig RU, Asif S, Meer AB. Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries. Comput Methods Programs Biomed. 2020; 186: 105198.
  • Revilla-León M, Gómez-Polo M, Vyas S. Artificial intelligence applications in restorative dentistry: A systematic review. J Prosthet Dent. 2021; S0022-3913(21)00087-1.
  • Aliaga IJ, Vera V, De Paz JF, García AE, Mohamad MS. Modelling the longevity of dental restorations by means of a CBR system. Biomed Res Int. 2015; 2015:540306.
  • Lu CH, Ko EWC, Liu L. Improving the video imaging prediction of postsurgical facial profiles with an artificial neural network. J Dent Sci. 2009; 4: 118–129.
  • Patcas R, Bernini D, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. 2019; 48: 77–83.
  • Patcas R, Timofte R, Volokitin A. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups. Eur J Orthod. 2019; 41: 428-433.
  • Kim BS, Yeom HG, Lee JH. Deep learning-based prediction of paresthesia after third molar extraction: A preliminary study. Diagnostics (Basel). 2021; 11: 1572.
  • Liu Z, Liu J, Zhou Z. Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs. Int J Comput Assist Radiol Surg. 2021; 16: 415-422.
  • Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics. J Orofac Orthop. 2020; 81:52–68.
  • Choi HI, Jung SK, Baek SH. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019; 30: 1986-1989.
  • Kim H, Shim E, Park J, Kim YJ, Lee U, Kim Y. Webbased fully automated cephalometric analysis by deep learning. Comput Methods Programs Biomed. 2020; 194: 105513.
  • Dobratulin K, Gaidel A, Aupova I, Ivleva A, Kapishnikov A, Zelter P. The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile. ArXiv. 2020; 01135: 1-5.
  • Kim MJ, Liu Y, Oh SH, Ahn HW, Kim SH, Nelson G. Evaluation of a multi-stage convolutional neural network-based fully automated landmark identification system using cone-beam computed tomographysynthesized posteroanterior cephalometric images. Korean J Orthod. 2021; 51: 77- 85.
  • Muraev AA, Tsai P, Kibardin I, Oborotistov N, Shirayeva T, Ivanov S, et al Frontal cephalometric landmarking: Humans vs artificial neural networks. Int J Comput Dent. 2020; 23: 139-148.
  • Amasya H, Cesur E, Yıldırım DOK. Validation of cervical vertebral maturation stages: artificial intelligence vs human observer visual analysis. Am J Orthod Dentofac Orthop. 2020; 158: 173-179.
  • Kök H, Acilar AM, İzgi MS. Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics. Prog Orthod. 2019; 20:41.
  • Guo YC, Han M, Chi Y. Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images. Int J Legal Med. 2021; 135: 1589-1597.
  • Thanathornwong B. Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment. Health Inform Res. 2018; 24: 22-28. 
  • Yoo JH, Yeom HG, Shin WS, Yun JP, Lee JH, Jeong SH, et al.Deep learning-based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals. BMC Oral Health. 2021; 21: 
  • Monill-González A, Rovira-Calatayud L, d'Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthod Craniofac Res. 2021; 24 Suppl 2: 6-15.
  • Wang X, Pastewait M, Wu T. 3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation. Orthod Craniofac Res. 2021; 1: 1-9.
  • Chen S, Wang L, Li G. Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients. Angle Orthod. 2020; 90: 77-84.
  • Umer F, Habib S. Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review. J Endod. 2022; 48: 152-160.
  • Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod. 2021; 47: 1352–1357.
  • Boreak N. Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review. J Contemp Dent Pract. 2020; 21: 926-934.
  • Saghiri MA, Garcia-Godoy F, Gutmann JL. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod. 2012; 38: 1130–1134.
  • Saghiri MA, Asgar K, Boukani KK. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 2012; 45: 257–265.
  • Mahmoud YE, Labib SS, Hoda MO. Clinical Prediction of Teeth Periapical Lesion based on Machine Learning Techniques – An Experimental Study. Proceedings of Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications. Available at: http://sdiwc.net/digital-library/clinical-prediction-of-teeth-periapical-lesion-based-on-machine-learning-techniques.html
  • Ekert T, Krois J, Meinhold L. Deep learning for the radiographic detection of apical lesions. J Endod. 2019; 45: 917–922.
  • Hiraiwa T, Ariji Y, Fukuda M. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019; 48: 20180218.
  • Johari M, Esmaeili F, Andalib A. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol. 2017; 46: 20160107.
  • Fukuda M, Inamoto K, Shibata N. Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2020; 36: 337-343.
  • Bindal P, Bindal U, Kazemipoor M, Kazemipoor M, Jha S. Hybrid machine learning approaches in viability assessment of dental pulp stem cells treated with platelet-rich concentrates on different periods. Appl Med Informatics. 2019; 41: 93-101.
  • Qu Y, Lin Z, Yang Z, Lin H, Huang X, Gu L. Machine learning models for prognosis prediction in endodontic microsurgery. J Dent. 2022; 118:103947.
  • Herbst CS, Schwendicke F, Krois J, Herbst SR. Association between patient-, tooth- and treatment-level factors and root canal treatment failure: A retrospective longitudinal and machine learning study. J Dent. 2022; 117: 103937.
  • Poswar F, Farias L, de Carvalho Fraga C, Bambirra W, Brito-Júnior M, Sousa-Neto M, et al. Bioinformatics, interaction network analysis, and neural networks to characterize gene expression of radicular cyst and periapical granuloma. J. Endod. 2015; 41: 877–883.
  • Zakirulla M, Javed S, Assiri H, Alqahtani M, Alzahrani H, Laheq T, et al. An overview of artificial neural network in the field of pediatric dentistry. J Dent Orofacial Res. 2020; 16: 20-25.
  • Kılıc MC, Bayrakdar IS, Çelik Ö. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021; 50: 20200172.
  • Tizhoosh HR, Pantanowitz L. Artificial Intelligence and digital pathology: Challenges and opportunities. J Pathol Inform. 2018; 9: 38.
آمار
تعداد مشاهده مقاله: 6,168
تعداد دریافت فایل اصل مقاله: 1,900


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب