Machine learning-based decision support system for orthognathic diagnosis and treatment planning.
Background: Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support syste...
| Publicado en: | BMC Oral Health Vol. 24; no. 1; pp. 1 - 14 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
BioMed Central
2/28/2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175797839&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175797839 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726831 1CIC jtl: BMC Oral Health issn: 14726831 maglogo: N pubinfo: dt: 2/28/2024 vid: 24 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 175797839 175797839 175797839 10.1186/s12903-024-04063-6 175797839 ppf: 1 ppct: 13 formats: tig: atl: Machine learning-based decision support system for orthognathic diagnosis and treatment planning. aug: au: Du, Wen Bi, Wenjun Liu, Yao Zhu, Zhaokun Tai, Yue Luo, En affil: State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, 610041, Chengdu, Sichuan, China sug: subj: Machine Learning Decision Support Systems, Clinical Orthognathic Surgery Diagnosis, Computer Assisted Maxillofacial Abnormalities Diagnosis Maxillofacial Abnormalities Surgery Tooth Abnormalities Diagnosis Tooth Abnormalities Surgery Human Sensitivity and Specificity ROC Curve Cephalometry Treatment Outcomes Descriptive Statistics User-Computer Interface Artificial Intelligence China Academic Medical Centers Algorithms Male Female Adult Paired T-Tests Intraclass Correlation Coefficient Funding Source Adult: 19-44 years Male Female ab: Background: Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations. Methods: Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. Results: The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups. Conclusions: Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|