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TRUVACE RECORD VERSION
record: TRV-2026-0899
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-26T06:06:59.391298Z
status: published
lens: g_space
sector: health
headline: Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs
dek: Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition and reader variability. This study developed stacked deep learning ensembles for automated detection and radiographic size stratification of periapical lesions. In total, 146 radiographs comprising normal cases and three lesion-size categories were cropped around the root apex and augmented using predefined transformations. Five convolutional neural network backbones were trained, and their probability out…
gain_title: Stacked ensembles combining five CNN backbones improved automated detection and size stratification of periapical lesions on cropped intraoral radiographs, reaching high accuracy and high sensitivity for very small lesions on internal testing.
problem_title: (none)
trace_subject: (none)
gain_reading: Stacked ensembles combining five CNN backbones improved automated detection and size stratification of periapical lesions on cropped intraoral radiographs, reaching high accuracy and high sensitivity for very small lesions on internal testing.
gain_evidence: both ensembles performed better than the individual backbones | MLR achieved an accuracy of 0.96 and a macro-averaged ROC-AUC of 1.00 | Both ensembles also achieved a sensitivity of 0.94 for lesions < 2 mm
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built stacked deep learning ensembles to detect periapical lesions and stratify them by radiographic size on intraoral radiographs. Using 146 cropped and augmented images, five CNN backbones were combined with MLR and XGBoost meta-learners and evaluated on an internal hold-out test set.

High internal accuracy and sensitivity for sub-2 mm lesions suggests potential value for radiographic decision support in endodontics, but the small single-centre design means results are preliminary. By the August 2026 publication date, no prospective multicentre external validation or clinical deployment outcome had been reported.
limitation: Findings are based on a small, single-centre internal hold-out set, so performance estimates may be optimistic and need prospective multicentre external validation before clinical use.
tag: Evidence-backed gain
key_points: Study used 146 radiographs covering normal cases and three lesion-size categories, cropped around the root apex and augmented with predefined transformations. | Five convolutional neural network backbones were trained and combined with MLR and XGBoost meta-learners. | Internal hold-out testing reported MLR accuracy 0.96 and macro-averaged ROC-AUC 1.00, XGBoost accuracy 0.95 with similar AUC.
rundown: The authors curated 146 intraoral radiographs and cropped them around the root apex, applying predefined augmentation. Five CNN backbones were trained to classify normal versus three lesion-size categories, then stacked using multinomial logistic regression and gradient boosting meta-learners.

On the internal hold-out test set both ensembles outperformed individual backbones, with MLR reporting 0.96 accuracy and 1.00 macro-averaged ROC-AUC and XGBoost 0.95 accuracy. Sensitivity for lesions under 2 mm was 0.94, addressing a known difficulty from anatomical superimposition and reader variability.
sources:
- peer_reviewed | Australian Endodontic Journal | https://doi.org/10.1111/aej.70113 | 2026-08-24
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