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Received: 2023-04-10

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Hai Ming WONG


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Journal of Zhejiang University SCIENCE B 2023 Vol.24 No.11 P.974-984


Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review

Author(s):  Jiajun ZHU, Yuxin YANG, Hai Ming WONG

Affiliation(s):  Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China; more

Corresponding email(s):   wonghmg@hku.hk

Key Words:  Facial morphology, Soft-tissue changes, Artificial intelligence (AI), Orthodontic treatment

Jiajun ZHU, Yuxin YANG, Hai Ming WONG. Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review[J]. Journal of Zhejiang University Science B, 2023, 24(11): 974-984.

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artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has not been systematically assessed. This scoping review was conducted to outline the development of AI in terms of predicting soft-tissue changes after orthodontic treatment, as well as to comprehensively evaluate its prediction accuracy. Six electronic databases (PubMed, EBSCOhost, Web of Science, Embase, Cochrane Library, and Scopus) were searched up to March 14, 2023. Clinical studies investigating the performance of AI-based systems in predicting post-orthodontic soft-tissue alterations were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Joanna Briggs Institute (JBI) appraisal checklist for diagnostic test accuracy studies were applied to assess risk of bias, while the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) assessment was conducted to evaluate the certainty of outcomes. After screening 2500 studies, four non-randomized clinical trials were finally included for full-text evaluation. We found a low level of evidence indicating an estimated high overall accuracy of AI-generated prediction, whereas the lower lip and chin seemed to be the least predictable regions. Furthermore, the facial morphology simulated by AI via the fusion of multimodality images was considered to be reasonably true. Since all of the included studies that were not randomized clinical trials (non-RCTs) showed a moderate to high risk of bias, more well-designed clinical trials with sufficient sample size are needed in future work.


摘要:近年来,人工智能(AI)被应用于分析和预测正畸面部软组织变化,然而其可靠性尚缺乏系统性评价。本综述概述了AI预测正畸面部变化的研究进展,并对其预测准确度进行综合分析。我们检索了包括PubMed、EBSCOhost、Web of Science、Embase、Cochrane Library和Scopus在内的6个电子数据库(检索日期截至2023年3月14日),纳入了所有使用AI系统对正畸面部变化进行预测的临床研究,并应用QUADAS-2评价表和JBI对诊断性试验的评价表对纳入研究进行偏倚风险分析,同时应用GRADE评价系统进行证据分级。在筛选了2500项研究后,最终有4项非随机临床试验被纳入全文评价。低水平证据表明,AI预测正畸面部变化的总体准确度很高,但其对于下唇和颏部的预测准确度较低。此外,AI通过多模态融合模拟预测的面部形态被认为是合理真实的。然而,由于所有纳入的非随机对照试验研究都显示出中度至高度偏倚风险,因此还需要更多更严谨的临床研究来证实AI在正畸面部变化预测方面的应用价值。


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