Automated classification of dental implant brands and prosthetic platform sizes on panoramic radiographs using deep learning

Abstract

Statement of problem

Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation—such as the implant passport or surgical report—is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction.

Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation—such as the implant passport or surgical report—is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction.

Purpose

The purpose of this study was to develop and validate an artificial intelligence (AI) algorithm capable of detecting dental implants on panoramic radiographs and classifying them by implant brand and prosthetic platform size.

The purpose of this study was to develop and validate an artificial intelligence (AI) algorithm capable of detecting dental implants on panoramic radiographs and classifying them by implant brand and prosthetic platform size.

Material and methods

A dataset of 387 panoramic radiographs with 1004 dental implant images was randomly divided into training, validation, and test sets using an 80/10/10 stratified split across 25 independent partitions. Convolutional neural network (CNN) architectures were developed and trained using manually annotated images. Ground truth labels for implant brand and prosthetic platform size were obtained from patient records.

A dataset of 387 panoramic radiographs with 1004 dental implant images was randomly divided into training, validation, and test sets using an 80/10/10 stratified split across 25 independent partitions. Convolutional neural network (CNN) architectures were developed and trained using manually annotated images. Ground truth labels for implant brand and prosthetic platform size were obtained from patient records.

Results

Among the evaluated architectures, a Faster region-based CNN (R-CNN) combined with an EfficientNet-B7 backbone demonstrated the highest diagnostic performance. Implant detection achieved a mean Intersection over Union (IoU) of 77.65% ±11.54% and an accuracy of 99.45% ±0.70% with an average of 0.60 ±0.76 false negatives per split (mean ±standard deviation across 25 partitions). Implant brand classification accuracy was 98.93% ±0.91% (Callus Pro), 97.81% ±1.43% (Denti Root Form), 98.85% ±1.19% (NobelReplace Conical Connection partially machined collar [PMC]), and 96.79% ±1.83% for implants of unknown brand, yielding an overall brand-classification accuracy of 95.66% ±2.02%. Platform-size classification accuracy was 87.62% ±2.94% for narrow, 87.78% ±3.15% for regular, and 96.79% ±1.83% for unknown sizes. Combined brand-and-platform classification achieved an overall accuracy of 85.60% ±3.27%.

Among the evaluated architectures, a Faster region-based CNN (R-CNN) combined with an EfficientNet-B7 backbone demonstrated the highest diagnostic performance. Implant detection achieved a mean Intersection over Union (IoU) of 77.65% ±11.54% and an accuracy of 99.45% ±0.70% with an average of 0.60 ±0.76 false negatives per split (mean ±standard deviation across 25 partitions). Implant brand classification accuracy was 98.93% ±0.91% (Callus Pro), 97.81% ±1.43% (Denti Root Form), 98.85% ±1.19% (NobelReplace Conical Connection partially machined collar [PMC]), and 96.79% ±1.83% for implants of unknown brand, yielding an overall brand-classification accuracy of 95.66% ±2.02%. Platform-size classification accuracy was 87.62% ±2.94% for narrow, 87.78% ±3.15% for regular, and 96.79% ±1.83% for unknown sizes. Combined brand-and-platform classification achieved an overall accuracy of 85.60% ±3.27%.

Conclusions

The 2-stage CNN pipeline demonstrated clinically acceptable accuracy for automated dental implant detection and classification tasks on panoramic radiographs, supporting its potential integration into clinical workflows to improve diagnostic efficiency and standardization.

The 2-stage CNN pipeline demonstrated clinically acceptable accuracy for automated dental implant detection and classification tasks on panoramic radiographs, supporting its potential integration into clinical workflows to improve diagnostic efficiency and standardization.