legacy implant tapered pro (Biohorizons)
90
Structured Review
Biohorizons
legacy implant tapered pro
Legacy Implant Tapered Pro, supplied by Biohorizons, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/legacy+implant+tapered+pro/tapered+internal+implants/pmc11049199-14-82-80
Average 90 stars, based on 1 article reviews
Legacy Implant Tapered Pro, supplied by Biohorizons, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/legacy+implant+tapered+pro/tapered+internal+implants/pmc11049199-14-82-80
Average 90 stars, based on 1 article reviews
legacy implant tapered pro - by Bioz Stars,
2026-09
90/100 stars
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1) Product Images from "Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review"
Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review
Journal: Diagnostics
doi: 10.3390/diagnostics14080806
Figure Legend Snippet: Study characteristics and accuracy results of the included studies.
Techniques Used: Indirect Immunoperoxidase Assay, Biomarker Discovery, Generated, Plasmid Preparation, Diagnostic Assay
Related Articles
Indirect Immunoperoxidase Assay:Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review Article Snippet: PA) AUC: 0.823 Sensitivity: 80.0% Specificity: 84.5% PPV: 83.8% NPV: 80.9% , Classification accuracy performance of DL was significantly superior. .. Hsiao et al., 2023, USA [ ] , - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 , - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image , PA , January 2011 to January 2019 , N = 788 , Implant Brands: N = 3 (A) Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Article Snippet: Author, Year Country Algorithm Network Architecture and Name Architecture Depth (Number of Layers), Number of Training Epochs, and Learning Rate Type of Radiographic Image Patient Data Collection/Xray Collection Duration Number of X-rays/Implant Images Evaluated (N) Number and Names of Implant Brands and Models Evaluated Comparator Test Group and Training/Validation Number and Ratio Accuracy Reported Authors Sugges-tions/Conclusions Hsiao et al., 2023, USA [63] - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image PA January 2011 to January 2019 N = 788 Implant Brands: N = 3 (A) Biomarker Discovery:Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review Article Snippet: PA) AUC: 0.823 Sensitivity: 80.0% Specificity: 84.5% PPV: 83.8% NPV: 80.9% , Classification accuracy performance of DL was significantly superior. .. Hsiao et al., 2023, USA [ ] , - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 , - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image , PA , January 2011 to January 2019 , N = 788 , Implant Brands: N = 3 (A) Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Article Snippet: Author, Year Country Algorithm Network Architecture and Name Architecture Depth (Number of Layers), Number of Training Epochs, and Learning Rate Type of Radiographic Image Patient Data Collection/Xray Collection Duration Number of X-rays/Implant Images Evaluated (N) Number and Names of Implant Brands and Models Evaluated Comparator Test Group and Training/Validation Number and Ratio Accuracy Reported Authors Sugges-tions/Conclusions Hsiao et al., 2023, USA [63] - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image PA January 2011 to January 2019 N = 788 Implant Brands: N = 3 (A) Generated:Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review Article Snippet: PA) AUC: 0.823 Sensitivity: 80.0% Specificity: 84.5% PPV: 83.8% NPV: 80.9% , Classification accuracy performance of DL was significantly superior. .. Hsiao et al., 2023, USA [ ] , - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 , - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image , PA , January 2011 to January 2019 , N = 788 , Implant Brands: N = 3 (A) Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Article Snippet: Author, Year Country Algorithm Network Architecture and Name Architecture Depth (Number of Layers), Number of Training Epochs, and Learning Rate Type of Radiographic Image Patient Data Collection/Xray Collection Duration Number of X-rays/Implant Images Evaluated (N) Number and Names of Implant Brands and Models Evaluated Comparator Test Group and Training/Validation Number and Ratio Accuracy Reported Authors Sugges-tions/Conclusions Hsiao et al., 2023, USA [63] - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image PA January 2011 to January 2019 N = 788 Implant Brands: N = 3 (A) Plasmid Preparation:Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review Article Snippet: PA) AUC: 0.823 Sensitivity: 80.0% Specificity: 84.5% PPV: 83.8% NPV: 80.9% , Classification accuracy performance of DL was significantly superior. .. Hsiao et al., 2023, USA [ ] , - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 , - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image , PA , January 2011 to January 2019 , N = 788 , Implant Brands: N = 3 (A) Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Article Snippet: Author, Year Country Algorithm Network Architecture and Name Architecture Depth (Number of Layers), Number of Training Epochs, and Learning Rate Type of Radiographic Image Patient Data Collection/Xray Collection Duration Number of X-rays/Implant Images Evaluated (N) Number and Names of Implant Brands and Models Evaluated Comparator Test Group and Training/Validation Number and Ratio Accuracy Reported Authors Sugges-tions/Conclusions Hsiao et al., 2023, USA [63] - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image PA January 2011 to January 2019 N = 788 Implant Brands: N = 3 (A) Diagnostic Assay:Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review Article Snippet: PA) AUC: 0.823 Sensitivity: 80.0% Specificity: 84.5% PPV: 83.8% NPV: 80.9% , Classification accuracy performance of DL was significantly superior. .. Hsiao et al., 2023, USA [ ] , - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 , - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image , PA , January 2011 to January 2019 , N = 788 , Implant Brands: N = 3 (A) Article Title: Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Article Snippet: Author, Year Country Algorithm Network Architecture and Name Architecture Depth (Number of Layers), Number of Training Epochs, and Learning Rate Type of Radiographic Image Patient Data Collection/Xray Collection Duration Number of X-rays/Implant Images Evaluated (N) Number and Names of Implant Brands and Models Evaluated Comparator Test Group and Training/Validation Number and Ratio Accuracy Reported Authors Sugges-tions/Conclusions Hsiao et al., 2023, USA [63] - 10 CNN architectures (1) MnasNet (2) ShuffleNet7 (3) MobileNet8 (4) AlexNet9 (5) VGG10 (6) ResNet11 (7) DenseNet12 (8) SqueezeNet13 (9) ResNeXt14 (10) Wide ResNet15 - Learning rate: 0.001 - For training accuracy, the CNN assessed data 90 times per image PA January 2011 to January 2019 N = 788 Implant Brands: N = 3 (A) |