Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review

Umer, Fahad; Habib, Saqib

doi:10.1016/j.joen.2021.11.007

Cited by 37 publications

(23 citation statements)

References 40 publications

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“…The pandemic situation may have forced an accelerated utilization of the smart technologies that are often combined with tele-health solutions [ 7 , 60 ]. Current high-quality publications regarding AI utilization in the category of Dentistry, Oral Surgery & Medicine registered by Web of Science—Core Collection include 24 review articles in the year 2021 and 5 review articles published in the year 2022 [ 60 , 191 , 192 , 193 ]. Of these, only one paper covered the whole dental field.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…The use of electronic dental records has been increasing and clinical data have been digitalized, which allows for the use of AI to interpret the data. According to Umer et al, modern medicine brought a revolution with the application of AI in clinical decision-making processes [ 35 ]. AI and machine-learning algorithms incorporate a computational model that utilizes and processes information on many datasets with critical parameters to predict an outcome.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Tooth Prognosis Using Artificial Intelligence

et al. 2022

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The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

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“…Two reviewers independently assessed the risk of bias of the included using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies—Revised (QUADAS-2) tool [ 17 ]. A checklist was compiled after consulting other similar study approaches [ 18 - 20 ]. A third reviewer resolved disagreements between both reviewers.…”

Section: Methodsmentioning

confidence: 99%

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

Ahmed¹,

Aziz²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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Background In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction. Objective The purpose of this study is to provide a systematic review complete with a quality assessment looking at diabetes effectiveness of using artificial intelligence (AI) in WDs for forecasting or predicting BG levels. Methods We searched 7 of the most popular bibliographic databases. Two reviewers performed study selection and data extraction independently before cross-checking the extracted data. A narrative approach was used to synthesize the data. Quality assessment was performed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Results From the initial 3872 studies, the features from 12 studies were reported after filtering according to our predefined inclusion criteria. The reference standard in all studies overall (n=11, 92%) was classified as low, as all ground truths were easily replicable. Since the data input to AI technology was highly standardized and there was no effect of flow or time frame on the final output, both factors were categorized in a low-risk group (n=11, 92%). It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). The wide usage of photoplethysmogram and near-infrared sensors was observed on wrist-worn devices. Conclusions This review has provided the most extensive work to date summarizing WDs that use ML for diabetic-related BG level forecasting or prediction. Although current studies are few, this study suggests that the general quality of the studies was considered high, as revealed by the QUADAS-2 assessment tool. Further validation is needed for commercially available devices, but we envisage that WDs in general have the potential to remove the need for invasive devices completely for glucose monitoring in the not-too-distant future. Trial Registration PROSPERO CRD42022303175; https://tinyurl.com/3n9jaayc

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Critical Analysis of Artificial Intelligence in Endodontics: A Scoping Review

Cited by 37 publications

References 40 publications

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

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

Diagnosis of Tooth Prognosis Using Artificial Intelligence

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

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