Table 2.
Author (Year) | Application | Imaging Modality | AI Technique | Image Data Set Used to Develop the AI Model | Independent Testing Image Data Set/Validation Technique | Performance |
---|---|---|---|---|---|---|
Ghazvinian Zanjani [24] (2019) | Tooth segmentation | Intraoral scanning | CNN | 120 scans, comprising 60 upper jaws and 60 lower jaws. | 5-fold CV | 0.94 (intersection over union score) |
Kim [45] (2020) | Tooth segmentation | Intraoral scanning | Generative adversarial network | 10,000 cropped images | Approximate 350 cropped images | An average improvement of 0.004 mm in the tooth segmentation |
Lian [25] (2020) | Tooth labelling | Intraoral scanning | CNN | 30 scans of upper jaws | 5-fold CV | 0.894 to 0.970 (DSC) |
Liu [27] (2016) | Identification of Autism Spectrum Disorder | Facial scanning | SVM | 87 scans from children with and without Autism Spectrum Disorder | LOOCV | 88.51% (accuracy) |
Knoops [26] (2019) | Diagnosis and planning in plastic and reconstructive surgery | Facial scanning | Machine-learning-based 3D morphable model | 4261 scans from healthy subjects and orthognathic patients | LOOCV | Diagnosis 95.5% (sensitivity); 95.2% (specificity) Surgical simulation 1.1 ± 0.3 mm (accuracy) |
3D, three-dimensional; AI, artificial intelligence; CV, cross-validation; DSC, dice similarity coefficient; LOOCV, leave-one-out cross-validation; SVM, support vector machine.