دورية أكاديمية
Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?
| العنوان: | Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods? |
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| المؤلفون: | Park JA, Moon JH, Lee JM, Cho SJ, Seo BM, Donatelli RE, Lee SJ |
| المصدر: | The Angle orthodontist [Angle Orthod] 2024 Sep 01; Vol. 94 (5), pp. 549-556. |
| نوع المنشور: | Journal Article; Comparative Study |
| اللغة: | English |
| بيانات الدورية: | Publisher: Edward H. Angle Society of Orthodontia Country of Publication: United States NLM ID: 0370550 Publication Model: Print Cited Medium: Internet ISSN: 1945-7103 (Electronic) Linking ISSN: 00033219 NLM ISO Abbreviation: Angle Orthod Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Appleton, Wis. [etc.] Edward H. Angle Society of Orthodontia. |
| مواضيع طبية MeSH: | Cephalometry*/methods , Orthognathic Surgical Procedures*/methods , Artificial Intelligence* , Anatomic Landmarks*, Humans ; Female ; Male ; Linear Models ; Treatment Outcome ; Adult ; Young Adult ; Adolescent ; Neural Networks, Computer ; Algorithms ; Retrospective Studies ; Least-Squares Analysis ; Forecasting |
| مستخلص: | Objectives: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Materials and Methods: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. Results: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. Conclusions: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes. (© 2024 by The EH Angle Education and Research Foundation, Inc.) |
| فهرسة مساهمة: | Keywords: Artificial intelligence; Deep learning; Machine learning; Partial least squares; Prediction; Surgery |
| تواريخ الأحداث: | Date Created: 20240904 Date Completed: 20240904 Latest Revision: 20240904 |
| رمز التحديث: | 20240904 |
| DOI: | 10.2319/111423-756.1 |
| PMID: | 39230019 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1945-7103 |
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| DOI: | 10.2319/111423-756.1 |