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

Healthcare, Journal Year: 2022, Volume and Issue: 10(7), P. 1269 - 1269

Published: July 8, 2022

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

Language: Английский

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Artificial Intelligence in Medicine and Dentistry

Acta Stomatologica Croatica, Journal Year: 2023, Volume and Issue: 57(1), P. 70 - 84

Published: March 15, 2023

Introduction: Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent.The first applications of AI were primarily academia and government research institutions, as technology advanced, also industry, commerce, medicine dentistry.Objective: Considering that the possibilities applying artificial are developing rapidly this field one areas with greatest increase number newly published articles, aim paper was to provide an overview literature give insight dentistry.In addition, discuss advantages disadvantages.Conclusion: The dentistry just being discovered.Artificial will greatly contribute developments dentistry, it a tool enables development progress, especially terms personalized healthcare lead much better treatment outcomes.

Language: Английский

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Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

Journal of Clinical Medicine, Journal Year: 2024, Volume and Issue: 13(9), P. 2709 - 2709

Published: May 4, 2024

Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging used to identify PLs. The aim this study was assess the diagnostic accuracy artificial intelligence (AI) software Diagnocat PL detection OPG CBCT images. Methods: included 49 patients, totaling 1223 teeth. Both images were analyzed by AI three experienced clinicians. All obtained one patient cohort, findings compared consensus human readers using CBCT. AI’s a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative (NPV), F1 score. Results: sensitivity 33.33% with an score 32.73%. For images, 77.78% 84.00%. specificity over 98% both Conclusions: demonstrated high detecting PLs but lower

Language: Английский

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A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study

Journal of Dentistry, Journal Year: 2021, Volume and Issue: 115, P. 103865 - 103865

Published: Oct. 26, 2021

Language: Английский

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Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

BMC Oral Health, Journal Year: 2023, Volume and Issue: 23(1)

Published: June 3, 2023

Abstract Background Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was develop an AI framework diagnose multiple dental diseases on PRs, and initially evaluate its performance. Methods developed based 2 deep convolutional neural networks (CNNs), BDU-Net nnU-Net. 1996 PRs were used for training. Diagnostic evaluation performed a separate dataset including 282 PRs. Sensitivity, specificity, Youden’s index, area under curve (AUC), diagnostic time calculated. Dentists with 3 different levels seniority (H: high, M: medium, L: low) diagnosed same independently. Mann-Whitney U test Delong conducted statistical analysis (ɑ=0.05). Results index diagnosing 5 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing 0.554, 0.990, 0.544 (caries), respectively. AUC 0.980 (95%CI: 0.976–0.983, impacted 0.975 0.972–0.978, full 0.935 0.929–0.940, residual 0.939 0.934–0.944, missing 0.772 0.764–0.781, caries), comparable that all dentists in roots (p > 0.05), values similar 0.05) or better than < M-level diseases. But statistically lower some H-level teeth, caries 0.05). mean significantly shorter 0.001). Conclusions nnU-Net demonstrated high specificity crowns, roots, efficiency. clinical feasibility preliminary verified since performance even 3–10 years experience. However, diagnosis should be improved.

Language: Английский

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Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review

Diagnostics, Journal Year: 2023, Volume and Issue: 13(3), P. 414 - 414

Published: Jan. 23, 2023

Technological advancements in health sciences have led to enormous developments artificial intelligence (AI) models designed for application sectors. This article aimed at reporting on the and performances of AI that been endodontics. Renowned online databases, primarily PubMed, Scopus, Web Science, Embase, Cochrane secondarily Google Scholar Saudi Digital Library, were accessed articles relevant research question published from 1 January 2000 30 November 2022. In last 5 years, there has a significant increase number applied developed determining working length, vertical root fractures, canal failures, morphology, thrust force torque preparation; detecting pulpal diseases; diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, case difficulty; segmenting pulp cavities. Most included studies (n = 21) using convolutional neural networks. Among studies. datasets used mostly cone-beam computed tomography images, followed by radiographs panoramic radiographs. Thirty-seven original fulfilled eligibility criteria critically assessed accordance with QUADAS-2 guidelines, which revealed low risk bias patient selection domain most (risk bias: 90%; applicability: 70%). The certainty evidence was GRADE approach. These can be as supplementary tools clinical practice order expedite decision-making process enhance treatment modality operation.

Language: Английский

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Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases

The Journal of the American Dental Association, Journal Year: 2023, Volume and Issue: 154(9), P. 795 - 804.e1

Published: July 14, 2023

Language: Английский

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Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry—A Narrative Review

Journal of Clinical Medicine, Journal Year: 2023, Volume and Issue: 12(23), P. 7378 - 7378

Published: Nov. 28, 2023

The concept of machines learning and acting like humans is what meant by the phrase “artificial intelligence” (AI). Several branches dentistry are increasingly relying on artificial intelligence (AI) tools. literature usually focuses AI models. These models have been used to detect diagnose a wide range conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous alveolar bone loss, need for orthodontic extractions or treatments, cephalometric analysis, age gender determination, more. primary contemporary applications in field undergraduate teaching research. Before these methods can be everyday dentistry, however, underlying technology user interfaces refined.

Language: Английский

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ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment

Clinical Oral Investigations, Journal Year: 2024, Volume and Issue: 28(4)

Published: March 22, 2024

Abstract Objectives The aim of the present consensus paper was to provide recommendations for clinical practice considering use visual examination, dental radiography and adjunct methods primary caries detection. Materials executive councils European Organisation Caries Research (ORCA) Federation Conservative Dentistry (EFCD) nominated ten experts each join expert panel. steering committee formed three work groups that were asked on (1) detection diagnostic methods, (2) activity assessment (3) forming individualised diagnoses. responsible “caries methods” searched evaluated relevant literature, drafted this manuscript made provisional recommendations. These discussed refined during structured process in whole group. Finally, agreement recommendation determined using an anonymous Delphi survey. Results Recommendations ( N = 8) approved agreed upon by panel: examination 3), 3) additional 2). While quality evidence found be heterogeneous, all Conclusion Visual is recommended as first-choice method lesions accessible surfaces. Intraoral radiography, preferably bitewing, method. Adjunct, non-ionising radiation might also useful certain situations. Clinical relevance panel merged from scientific literature with practical considerations provided their daily practice.

Language: Английский

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Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis

Japanese Dental Science Review, Journal Year: 2024, Volume and Issue: 60, P. 128 - 136

Published: Feb. 29, 2024

The accuracy of artificial intelligence-aided (AI) caries diagnosis can vary considerably depending on numerous factors. This review aimed to assess the diagnostic AI models for detection and classification bitewing radiographs. Publications after 2010 were screened in five databases. A customized risk bias (RoB) assessment tool was developed applied 14 articles that met inclusion criteria out 935 references. Dataset sizes ranged from 112 3686 While 86 % studies reported a model with an ≥80 %, most exhibited unclear or high bias. Three compared model's performance dentists, which consistently showed higher average sensitivity. Five included bivariate random-effects meta-analysis overall detection. odds ratio 55.8 (95 CI= 28.8 – 108.3), summary sensitivity specificity 0.87 (0.76 0.94) 0.89 (0.75 0.960), respectively. Independent meta-analyses dentin enamel conducted sensitivities 0.84 (0.80 0.87) 0.71 (0.66 0.75), Despite promising models, lack high-quality, adequately reported, externally validated highlight current challenges future research needs.

Language: Английский

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