Artificial intelligence for predicting the aesthetic component of the index of orthodontic treatment need

Stetzel, Leah, Foucher, Florence, Jang, Seung Jin, Wu, Tai-Hsien, Fields, Henry, Schumacher, Fernanda, Richmond, Stephen ORCID: https://orcid.org/0000-0001-5449-5318 and Ko, Ching-Chang 2024. Artificial intelligence for predicting the aesthetic component of the index of orthodontic treatment need. Bioengineering 11 (9) , 861. 10.3390/bioengineering11090861

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Abstract

The aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) is internationally recognized as a reliable and valid method for assessing aesthetic treatment need. The objective of this study is to use artificial intelligence (AI) to automate the AC assessment. A total of 1009 pre-treatment frontal intraoral photos with overjet values were collected. Each photo was graded by an experienced calibration clinician. The AI was trained using the intraoral images, overjet, and two other approaches. For Scheme 1, the training data were AC 1–10. For Scheme 2, the training data were either the two groups AC 1–5 and AC 6–10 or the three groups AC 1–4, AC 5–7, and AC 8–10. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were measured for all approaches. The performance was tested without overjet values as input. The intra-rater reliability for the grader, using kappa, was 0.84 (95% CI 0.76–0.93). Scheme 1 had 77% sensitivity, 88% specificity, 82% accuracy, 89% PPV, and 75% NPV in predicting the binary groups. All other schemes offered poor tradeoffs. Findings after omitting overjet and dataset supplementation results were mixed, depending upon perspective. We have developed deep learning-based algorithms that can predict treatment need based on IOTN-AC reference standards; this provides an adjunct to clinical assessment of dental aesthetics.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Dentistry
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	9 September 2024
Date of Acceptance:	20 August 2024
Last Modified:	09 Sep 2024 09:15
URI:	https://orca.cardiff.ac.uk/id/eprint/171929

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