Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies

被引:4
作者
Alam, Mohammad Khursheed [1 ,2 ,3 ]
Alftaikhah, Sultan Abdulkareem Ali [1 ]
Issrani, Rakhi [1 ]
Ronsivalle, Vincenzo [4 ]
Lo Giudice, Antonino [4 ]
Cicciu, Marco [3 ]
Minervini, Giuseppe [5 ,6 ]
机构
[1] Jouf Univ, Coll Dent, Prevent Dent Dept, Sakaka 72345, Saudi Arabia
[2] Saveetha Inst Med & Tech Sci, Saveetha Dent Coll & Hosp, Dept Dent Res Cell, Chennai 600077, Tamil Nadu, India
[3] Daffodil Int Univ, Fac Allied Hlth Sci, Dept Publ Hlth, Dhaka 1207, Bangladesh
[4] Catania Univ, Dept Biomed & Surg & Biomed Sci, I-95123 Catania, Italy
[5] Univ Campania Luigi Vanvitelli, Multidisciplinary Dept Med Surg & Odontostomatol S, I-80121 Naples, Italy
[6] Saveetha Univ, Saveetha Dent Coll & Hosp, Saveetha Inst Med & Tech Sci SIMATS, Chennai, Tamil Nadu, India
关键词
Artificial intelligence; Dental imaging; In -vitro studies; Systematic review; Meta; -analysis; Diagnostic accuracy; Precision; Time efficiency; DENTAL PANORAMIC RADIOGRAPHS; BONE; DIAGNOSIS; IMPLANT; IMMEDIATE; TEETH; ARCH;
D O I
10.1016/j.heliyon.2024.e24221
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies. Methods: A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings. Results: 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications. Conclusion: In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.
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页数:13
相关论文
共 66 条
[21]   Clinical, Radiological, and Aesthetic Outcomes after Placement of a Bioactive-Surfaced Implant with Immediate or Delayed Loading in the Anterior Maxilla: 1-Year Retrospective Follow-Up Study [J].
Iacono, Roberta ;
Mayer, Yaniv ;
Marenzi, Gaetano ;
Ferreira, Balan Vitor ;
Pires, Godoy Eduardo ;
Migliorati, Marco ;
Bagnasco, Francesco .
PROSTHESIS, 2023, 5 (03) :610-621
[22]  
Inchingolo F, 2010, INT J MED SCI, V7, P378
[23]  
Isola G, 2017, MINERVA STOMATOL, V66, P1, DOI [10.23736/S0926-4970.16.03995-3, 10.23736/S0026-4970.17.03995-4]
[24]   Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women [J].
Kavitha, Muthu Subash ;
An, Seo-Young ;
An, Chang-Hyeon ;
Huh, Kyung-Hoe ;
Yi, Won-Jin ;
Heo, Min-Suk ;
Lee, Sam-Sun ;
Choi, Soon-Chul .
ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY, 2015, 119 (03) :346-356
[25]  
Khatoonabad MJ, 2011, IRAN J RADIOL, V8, P23
[26]   Computerized Bone Age Estimation Using Deep Learning-Based Program: Evaluation of the Accuracy and Efficiency [J].
Kim, Jeong Rye ;
Shim, Woo Hyun ;
Yoon, Hee Mang ;
Hong, Sang Hyup ;
Lee, Jin Seong ;
Cho, Young Ah ;
Kim, Sangki .
AMERICAN JOURNAL OF ROENTGENOLOGY, 2017, 209 (06) :1374-1380
[27]  
Kwon AY, 2017, IMAGNG SCI DENT, V47, P87, DOI 10.5624/isd.2017.47.2.87
[28]   Surgical Risk on Patients with Coagulopathies: Guidelines on Hemophiliac Patients for Oro-Maxillofacial Surgery [J].
Laino, Luigi ;
Cicciu, Marco ;
Fiorillo, Luca ;
Crimi, Salvatore ;
Bianchi, Alberto ;
Amoroso, Giulia ;
Monte, Ines Paola ;
Herford, Alan Scott ;
Cervino, Gabriele .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 16 (08)
[29]   Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs [J].
Larson, David B. ;
Chen, Matthew C. ;
Lungren, Matthew P. ;
Halabi, Safwan S. ;
Stence, Nicholas V. ;
Langlotz, Curtis P. .
RADIOLOGY, 2018, 287 (01) :313-322
[30]   Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study [J].
Lee, Jae-Seo ;
Adhikari, Shyam ;
Liu, Liu ;
Jeong, Ho-Gul ;
Kim, Hyongsuk ;
Yoon, Suk-Ja .
DENTOMAXILLOFACIAL RADIOLOGY, 2019, 48 (01)