Ebd spotlight: the diagnostic accuracy of artificial intelligence in orthodontic extraction decisions

You have full access to this article via your institution. Download PDF MANAS DAVE reflects on topics discussed in our sister journal _Evidence-Based Dentistry._ A_ccuracy of artificial

intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis_ was published in the _Journal of Clinical Oral Investigations_ in 2022.1 A

commentary of this article was published in _Evidence-Based Dentistry_.2 AUTHOR INFORMATION Dr Manas Dave qualified from the University of Manchester with degrees in pathology and dentistry.

He undertook postgraduate training in Newcastle and Middlesbrough before returning to Manchester where he is an NIHR Academic Clinical Fellow in Oral and Maxillofacial Pathology and

Honorary Lecturer in Dentistry. Manas has achieved postgraduate qualifications in Medical Education, Dental Public Health and Pathology Informatics. He has published extensively across

numerous journals including the BMJ, Lancet and BDJ, has extensive teaching experience of both undergraduate and postgraduate students and is a recipient of numerous personal and research

awards. BACKGROUND Extraction of teeth in orthodontics is commonly undertaken for severe cases of dental and skeletal class II malocclusion, crowding, overbite, open-bite and midline

deviations amongst others.1,3 Extracting a tooth for an orthodontic treatment plan is a major treatment decision given the irreversible nature of the procedure. > Further research will be

 needed to highlight the integration of AI > in diagnostic and treatment algorithms and crucially determine the > limitations which could affect patient safety and robustness of > 

treatment planning. Artificial intelligence (AI) systems have been developed to aid in decision making across different fields of healthcare. Input data includes clinical records which

allows the algorithms to suggest or decline extractions for orthodontic treatment plans.1 Therefore, the aim of this systematic review was to determine the accuracy of AI in decision-making

for tooth extractions in orthodontics. METHODS An electronic database search of PubMed, Embase, Lilacs, Web of Science, Scopus, LIVIVO, Computers & Applied Science, ACM Digital Library

and Compendex were conducted in July 2021. Additionally, the grey literature was searched through Open Grey, Google Scholar and ProQuest Dissertation and Thesis. Only primary research

studies that investigated AI-based models for decision-making on tooth extraction in orthodontics with information on its accuracy were included. There were no restrictions on patient age,

time of study nor language. Quality assessment was undertaken using the QUADAS-2 tool. RESULTS * Six publications were included in this systematic review which were undertaken in the United

States of America (n = 1), South Korea (n = 1), China (n = 2), Italy (n = 1) and Japan (n = 1) with a combined total of 1,732 orthodontic patient records * There were four different methods

of AI used in the studies: ensemble learning/random forest, artificial neural network/multilayer perceptron, machine learning/back propagation and machine learning/feature vectors * Two

studies showed a high risk of bias in one domain and the rest had low or unclear risk of bias. No study satisfied all domains for a low risk of bias * A meta-analysis of all studies showed

an accuracy value of 0.87 (95% CI = 0.79-0.94) * The studies which used algorithms of multilayer perceptron and back propagation were pooled, resulted in accuracy values of 0.89 (95% CI =

0.70-1.00) and 0.88 (95% CI = 0.73-0.80) respectively. Random forest and feature vector algorithms were excluded from subgroup quantitative analysis * The I2 index showed heterogeneity

between all studies at 92% (p <0.001) * The overall sensitivity rate for AI on making decisions on tooth extraction for orthodontic treatment planning was 0.84 (95% CI = 0.58-1.00) and

the specificity rate was 0.89 (95% CI = 0.74-0.98). CONCLUSIONS The authors concluded: '…Decisions on tooth extraction using artificial intelligence presented an overall good accuracy

(0.87), showing similar results with different algorithms…' COMMENTS This systematic review provides a comprehensive and detailed search strategy to identify the evidence that is

currently available in AI with respect to orthodontics and decisions on tooth extractions. The included studies used different modes of artificial intelligence; hence, different algorithms

were processing the clinical information. The inherent methodological differences of the included studies limit pooling of the data and the validity of the overall conclusions. Additionally,

two of the studies had a high risk of bias and could have been excluded from quantitative analysis. This systematic review highlights the potential for the application of AI in the

decision-making process of orthodontic extractions. As AI applications become more accessible, further research will be needed to highlight the integration of AI in diagnostic and treatment

algorithms and crucially determine the limitations which could affect patient safety and robustness of treatment planning. REFERENCES * Evangelista K, de Freitas Silva B S, Yamamoto-Silva F

P _et al._ Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis. _Clin Oral Investig_ 2022; 26: 6893-6905. *

Thirumoorthy S, Gopal S. Diagnostic accuracy of AI in orthodontic extraction decisions: 'Are we ready to let Mr. Data run our Enterprise?' A commentary on a systematic review.

_Evid Based Dent_ 2023; 24: 17-18. * Travess H, Roberts-Harry D, Sandy J. Orthodontics. Part 8: Extractions in orthodontics. _Br Dent J_ 2004; 196: 195-203. Download references AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Academic Clinical Fellow Oral Maxillofacial Pathology, Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health,

University of Manchester, Manchester Academic Health Science Centre, Oxford Road, Manchester, M13 9PL, UK Manas Dave Authors * Manas Dave View author publications You can also search for

this author inPubMed Google Scholar RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Dave, M. EBD spotlight: The diagnostic accuracy of artificial

intelligence in orthodontic extraction decisions. _BDJ Team_ 10, 38–39 (2023). https://doi.org/10.1038/s41407-023-1857-7 Download citation * Published: 19 May 2023 * Issue Date: 19 May 2023

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