State-of-the-art deep learning for dental implant detection: a standardized benchmark for clinical practice

This is the authors' abstract. We don't add key points for this paper.

Purpose: Although deep-learning approaches show promise for detecting dental implants, the absence of standardized benchmarks has hindered evidence-based model selection.This study systematically compared five contemporary architectures on the same dataset under controlled conditions.A three-seed training-stability analysis was also performed to characterize between-run variability.Methods: YOLOv8-Large, YOLOv11-Large, real-time detection transformer (RT-DETR)-Large, lightweight detection transformer (LW-DETR)-Large, and Faster R-CNN ResNet50-FPN were evaluated using 3,996 annotated panoramic radiographs divided into training, validation, and test sets at an 80:10:10 ratio.Each model was trained using three random seeds to assess training stability.Detection accuracy, measured using the F1 score and mean average precision (mAP), end-to-end pipeline speed, measured using frames per second and latency, and graphics processing unit memory use were evaluated.Pairwise statistical comparisons were performed using the independent-samples t-test with Bonferroni correction.To ensure strict comparability among architectures, all mAP values were calculated using a single unified evaluator, pycocotools COCOeval, applied identically to every model.Results: YOLOv11-Large achieved the highest mAP@0.5 (0.9873±0.0004) and the fastest end-to-end processing speed (87.2 frames per second), with an F1 score of 0.9853±0.0010.YOLOv8-Large performed similarly, with an mAP@0.5 of 0.9859±0.0005and an F1 score of 0.9868±0.0002; the 95% confidence intervals for mAP@0.5 overlapped between the two models.RT-DETR-Large had the third-highest accuracy (mAP@0.5 = 0.9806±0.0055).LW-DETR-Large achieved an mAP@0.5 of 0.9699±0.0025but had substantially slower inference (7.1 frames per second), attributable to quadratic attention scaling at a resolution of 1,536×1,536 pixels.Faster R-CNN ResNet50-FPN had lower accuracy (mAP@0.5 = 0.9214±0.0051)and processing speed (14.2 frames per second).All four contemporary detectors significantly outperformed Faster R-CNN ResNet50-FPN (Bonferroni-corrected P<0.005).