Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025
Development and evaluation of an AI model for dental implant type detection: A comparison of diagnostic accuracy between a deep learning model and dental professionals.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Ahmed Walaa Magdy, Azhari Amr Ahmed, Almufti Abdulrahman, Alsadah Zainab Majed, Ahmed Amr Fawzy Abdelhamid, Lahiq Anas, Fawaz Khaled Ahmed
Аннотация
PURPOSE: To develop a deep-learning system for identifying five dental implant brands from periapical radiographs and compare its diagnostic accuracy with dental professionals and evaluate successive You Only Look Once (YOLO) architectures (v7-v12) to justify model selection.
MATERIALS AND METHODS: A dataset of 5851 periapical radiographs was compiled and divided into training, validation, and test partitions (80/10/10). After filtering to five brands (Adin, Dentium, Noris, OSSTEM, and Straumann), YOLO-based object detection models (versions 7-12) were trained and tested under identical conditions. The YOLOv12x model was adopted for final evaluation based on its optimal balance of accuracy and inference speed. Human performance was assessed using 100 held-out test images (20 per brand) via a multiple-choice web survey distributed to six clinician groups. Diagnostic metrics included mean average precision at IoU 0.50 (mAP@50), mAP@50-95, precision, recall, and accuracy.
RESULTS: The model achieved an mAP@50 of 0.989 (98.9%), an mAP@50-95 of 0.900 (90.0%), a precision of 0.969 (96.9%), and a recall of 0.977 (97.7%) across brands. Across YOLO generations, performance improved from mAP@50-95 = 0.817 (YOLOv7) to 0.905 (YOLOv12x). Fifty-two clinicians completed 5,200 image evaluations; the model significantly outperformed all clinician subgroups (one-way ANOVA with Tukey HSD, p < 0.001). Transformer-based DF-DETR achieved mAP@50-95 = 0.878, confirming YOLOv12x's superior efficiency-accuracy trade-off.
CONCLUSIONS: A high-performing model identified implant brands on periapical radiographs and outperformed clinicians across experience levels. Comparative analysis across YOLO architectures validated its measurable advantage in accuracy and speed. Lack of external validation and dataset imbalance are important limitations; future work will include external, multisite data and human-AI workflow evaluation.
MATERIALS AND METHODS: A dataset of 5851 periapical radiographs was compiled and divided into training, validation, and test partitions (80/10/10). After filtering to five brands (Adin, Dentium, Noris, OSSTEM, and Straumann), YOLO-based object detection models (versions 7-12) were trained and tested under identical conditions. The YOLOv12x model was adopted for final evaluation based on its optimal balance of accuracy and inference speed. Human performance was assessed using 100 held-out test images (20 per brand) via a multiple-choice web survey distributed to six clinician groups. Diagnostic metrics included mean average precision at IoU 0.50 (mAP@50), mAP@50-95, precision, recall, and accuracy.
RESULTS: The model achieved an mAP@50 of 0.989 (98.9%), an mAP@50-95 of 0.900 (90.0%), a precision of 0.969 (96.9%), and a recall of 0.977 (97.7%) across brands. Across YOLO generations, performance improved from mAP@50-95 = 0.817 (YOLOv7) to 0.905 (YOLOv12x). Fifty-two clinicians completed 5,200 image evaluations; the model significantly outperformed all clinician subgroups (one-way ANOVA with Tukey HSD, p < 0.001). Transformer-based DF-DETR achieved mAP@50-95 = 0.878, confirming YOLOv12x's superior efficiency-accuracy trade-off.
CONCLUSIONS: A high-performing model identified implant brands on periapical radiographs and outperformed clinicians across experience levels. Comparative analysis across YOLO architectures validated its measurable advantage in accuracy and speed. Lack of external validation and dataset imbalance are important limitations; future work will include external, multisite data and human-AI workflow evaluation.