aceDifferential-fusion YOLO for MB2 canal detection and recognition in cone-beam computed tomographic images
This study aimed to develop and validate an artificial intelligence (AI)-based system, called Differential-Fusion YOLO (DF-YOLO), for MB2 canal detection and recognition in cone-beam computed tomography (CBCT) images. DF-YOLO is a dual-branch convolutional architecture that integrates differential feature extraction with adaptive fusion to highlight subtle anatomical variations between adjacent slices. A clinical CBCT dataset comprising 148 microscopically confirmed MB2-positive cases (7,804 axial slices) was re…
The DF-YOLO model detected MB2 canal structures on CBCT slices with 87.4% recall and, in an exploratory patient-level test, 93.8% sensitivity, and it ran faster than manual slice annotation.
In the exploratory patient-level analysis, specificity was only 60.0% in a small MB2-negative cohort, so the model may flag canals in cases without them.
The study is retrospective with small test and reader datasets and few negative cases. It lacks external validation, so the authors say it is not ready for clinical implementation.
Evidence
- Peer-reviewedBMC Oral Health2026-10-19
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Truvace Impact Record TRV-2026-2005, v1: “Differential-fusion YOLO for MB2 canal detection and recognition in cone-beam computed tomographic images.” Truvace, 2026-10-20. /record/TRV-2026-2005 (accessed at citation time). sha256 13ae68a053fcca0c…
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