Journal of Dental Implant Research :eISSN 2671-941X / pISSN

 

Table. 1.

Summary of artificial intelligence articles in identification of dental implants system

Author and year Implant system Data modality Sample size Training Validation method AI method Accuracy (%) Sensitivity (%) Specificity (%) AUC
Lee et al.24) 2020 3 types (Osstem TSIII, Dentium Superline, Straumann Bone level) Panorama, periapical 10770 1000 epoch 10-fold cross-validation Pretrained CNN (GoogLeNet Inception-v3) 99.5 95.3 97.6 0.971
Takahashi et al.25) 2020 6 types (Nobel Biocare MK III/IIIG/MKIV/SG, Starumann Bone level, GC Genesio) Panorama 1282 1000 epoch Pretrained CNN (YOLO) 51~85 50~82 0.72
Kim et al.26) 2020 4 types (Nobel Biocare MK TiUnite, Dentium Implantium, Straumann Bone level/Tissue level) Periapical 801 500 epoch k-fold cross-validation Pretrained CNN (SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50) 93~98 92~98 94~98
Sukegawa et al.27) 2020 11 types (Zimmer Full Osseotite, Dentsply Astra EV/TX/Microthread, Nobel Biocare MKIII/SG/CC, Kyocera Finesia, Straumann Tissue level) Panorama 8859 700 epoch 4-fold cross-validation Pretrained CNN (VGG16 and VGG19) 86.0~93.5 84.2~92.8 80.2~90.7 0.958~1.000
Journal of Dental Implant Research 2020;39:48~52 https://doi.org/10.54527/jdir.2020.39.4.48
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