Uncertainty and Geometric Image-Region Ranking in Artificial Intelligence-Based Panoramic Tooth Segmentation: A Multi-Source External Benchmark

Background/Objectives: This retrospective benchmark evaluated scores for selecting regions of panoramic tooth segmentations for review. Methods: Three-member U-Net ensembles trained on UFBA-425 were evaluated on 590 held-out images and subsequently on 329 photographed radiographs. The original mixed-granularity pipeline comparison used the calibrated residual ensemble on 100 pediatric images. Cells in a 2 × 4 grid received regional mutual information (MI) scores but shared one whole-image soft Dice confidence (SDC) value. A lower Dice-risk area under the risk–coverage curve (AURC) indicated better ranking against source masks. Interaction tests, quality subset analyses, and prediction-positive sensitivity analyses were post hoc. Image-key bootstrap intervals may be too narrow for patient-level inference because patient independence was unverified. Results: The primary MI-minus-SDC AURC difference was +0.007 (95% confidence interval −0.033 to +0.041), leaving the comparison inconclusive. Subsequent evaluations were exploratory. Analyses of reference-positive cells (containing reference tooth pixels) received co-principal emphasis after outcomes were known. MI had a higher Dice-risk AURC than the replicated SDC in the pediatric main subset (+0.238) and the local SDC in AKUDENTAL (+0.157 residual, +0.134 ResNet18). When 20% of grid cells were reviewed, the mean maximum pixel probability (MSP) captured more error pixels than MI in seven of ten source/model comparisons, including both AKUDENTAL ensembles. On photographed radiographs, the MSP led by 15.73 percentage points for the residual ensemble and MI by 3.66 for ResNet18. For ResNet18, equal weighting of false-positive and false-negative capture favored MSP by 6.59 points. Conclusions: The primary comparison established neither superiority nor equivalence. Exploratory capture varied by source, model, and error mix.