ArticlePloS one2026
RCTE: A multi-class object detection framework for dental panoramic radiographs.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDental panoramic radiographs play an important role in oral disease screening, computer-aided diagnosis, and clinical treatment planning. However, accurate multi-class detection remains challenging due to small lesion sizes, ambiguous anatomical boundaries, insufficient multi-scale feature representation, and missed detections of complex dental structures.
objectiveThis study aims to develop an accurate and efficient multi-class object detection framework for dental panoramic radiographs to improve the detection performance of small lesions, weak-boundary structures, and complex anatomical targets.
methodsA novel re-parameterized cross-scale attention-enhanced framework, named RCTE, was proposed based on the YOLOv8n detector. The proposed framework integrates three complementary components: a Cross-Scale Channel Transformer (CSCT) module for cross-scale contextual interaction among P3, P4, and P5 features, a RepNCSPELAN4-based Re-parameterized Feature Pyramid Fusion (RPF) structure for enhancing multi-scale feature aggregation, and a Multi-Scale EMA (MS-EMA) mechanism for feature recalibration before the detection head. Experiments were conducted on a publicly available dental panoramic radiograph dataset containing 11 categories of dental structures and lesions. Model performance was evaluated using Precision, Recall, F1-score, mAP50, mAP75, and mAP50-95.
resultsCompared with the original YOLOv8n baseline, RCTE improved mAP50, mAP75, mAP50-95, and Recall by 2.55, 3.88, 2.40, and 5.10 percentage points, respectively. The proposed framework achieved better detection completeness and localization accuracy compared with other YOLO-based detectors. Furthermore, RCTE maintained real-time inference capability, demonstrating a favorable balance between detection accuracy and computational efficiency.
conclusionThe proposed RCTE framework effectively improves multi-class object detection performance in dental panoramic radiographs by enhancing cross-scale feature interaction, multi-scale feature fusion, and detection feature recalibration. This method provides a potential solution for computer-aided dental image analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.