Artificial intelligence in dentistry

Meghil, Mohamed M.; Rajpurohit, Pragya; Awad, Mohamed E.; McKee, Joshua; Shahoumi, Linah A.; Ghaly, Mira

doi:10.1016/j.dentre.2021.100009

Cited by 15 publications

(18 citation statements)

References 31 publications

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“…Recently, it has started to be used in dentistry, which has resulted in exceptional achievements [ 5 ]. AI is an efficient method for analyzing clinical dental data [ 8 ]. AI developments hint at potential advantages for health care, including fewer postoperative complications, higher quality of life, better decision-making, and far fewer needless procedures [ 7 ].…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence in Dentistry: Past, Present, and Future

Agrawal¹,

Nikhade²

2022

Cureus

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Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decisionmaking in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.

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Section: Reviewmentioning

confidence: 99%

Artificial Intelligence in Dentistry: Past, Present, and Future

Agrawal¹,

Nikhade²

2022

Cureus

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“…Periodontitis is a major public health problem due to its high prevalence and because it can lead to tooth loss and later to the loss of all teeth. AI has received enormous attention and has gone through a transition stage from being a purely statistical tool to being one of the main drivers of modern medicine [ 118 ]. AI software was designed with automated search algorithms with increased capability to extract, analyze, and correlate large quantities of relevant, multifactorial complications, as well as sub-sorting the predisposing factors of any investigated condition [ 119 ].…”

Section: Resultsmentioning

confidence: 99%

“…AI-based models are very efficient in determining the apical foramen and working length. These models can be of great assistance for less experienced dentists and non-specialists, as they can be used in clinical applications [ 118 , 125 ]. Artificial intelligence technology is widely used in the diagnosis (radiographic detection, CBCT images) of periapical pathologies, demonstrating satisfactory results with high sensitivity and moderate specificity [ 127 , 128 , 129 ].…”

Section: Resultsmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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“…It also enables accurate diagnosis and treatment planning. 24 25 The main limitation of this study was the extrusive mechanics of orthodontic treatment. In addition, the two-dimensional radiographic analysis using lateral cephalogram has limited accuracy and is prone to errors and superimposition, despite being the most popular diagnostic aid used in orthodontics clinics for treatment planning.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Vertical Skeletal Changes after Extraction and Nonextraction Orthodontic Treatment

et al. 2022

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Objectives Due to the constant battle regarding the controversial topic of orthodontic extraction, this study aims to assess the changes in vertical dimensions of patients treated with premolar extractions compared with nonextraction orthodontic patients. Materials and Methods A sample of 60 borderline patients were recruited and divided into extraction and nonextraction groups. Eleven pretreatment cephalometric measurements were recorded using WebCeph and patients were followed-up until the completion of treatment. Statistical Analysis Intragroup and intergroup comparisons were made using paired t-test and two-sample independent t-test, respectively. The joint significance of differences was measured using F-tests. Results The intragroup comparison revealed that in the extraction group, the vertical dimension was significantly increased posttreatment for four cephalometric measurements, that is, mandibular plane angle (p < 0.05), palatal plane angle (p < 0.05), Frankfort mandibular plane angle (p < 0.05), and y-axis (p < 0.05). In the comparison of the posttreatment values of both groups, the mean differences of the posttreatment values for sella nasion (SN)-gonion (Go)-gnathion (Gn) angle (p = 0.008), the total anterior (p = 0.050), and lower anterior facial heights (AFH; p = 0.011) were significantly higher. At the same time, the Jarabak ratio was significantly (p = 0.006) lower in the extraction group than in the nonextraction group. Conclusion The increase in vertical dimension is significantly higher in the extraction group than in the nonextraction group which indicates a significant impact of orthodontic extraction on the vertical dimensions.

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Artificial intelligence in dentistry

Cited by 15 publications

References 31 publications

Artificial Intelligence in Dentistry: Past, Present, and Future

Artificial Intelligence in Dentistry: Past, Present, and Future

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Vertical Skeletal Changes after Extraction and Nonextraction Orthodontic Treatment

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