Background/Objectives: Apical periodontitis is among the most prevalent dental pathologies, and its radiographic severity is graded on panoramic radiographs using the ordinal periapical index (PAI 3 < 4 < 5). Existing computer-aided systems typically treat these grades as unrelated classes and down-sample the radiograph to a fixed input, which erases the small periradicular radiolucencies that define early disease. This study evaluated a lesion-preserving, ordinal-aware YOLO framework for the simultaneous detection and severity grading of periapical lesions. Methods: A total of 3924 expert-annotated panoramic radiographs (PAI 3/4/5) were processed with an adaptive lesion-preserving tiling algorithm that mapped each radiograph onto native-resolution 640 × 640 tiles so that every lesion appeared intact within exactly one tile, yielding 4641 tiles. Six consecutive YOLO generations (YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO12, and YOLO26) were benchmarked under one identical pipeline on a locked hold-out test set of 985 lesions, of which 628 were PAI 3, 274 PAI 4, and 83 PAI 5. YOLO12m, which attained the highest point estimates and the smallest run-to-run variation, was then progressively augmented with an ordinal-aware detection loss, a high-resolution P2 head, severity class-weighting and test-time augmentation. Localization was measured with COCO mAP and severity grading with severity-weighted average precision (Sev-wAP), quadratic weighted kappa (QWK), a catastrophic off-by-two error rate, and per-grade recall. Results: Lesion-preserving tiling improved detection for every generation, raising the mAP@0.50 of YOLO12m from 0.494 to 0.615, the highest point estimate among the six generations, although the margin over the next two was within the resolution of this test set. Cumulatively adding the ordinal loss, the P2 head, severity weighting, and test-time augmentation raised mAP@0.50 to 0.664, mAP@0.50:0.95 to 0.333, Sev-wAP to 0.704, and QWK to 0.780. PAI 5 average precision rose from 0.268 to 0.331 and PAI 5 recall from 0.602 to 0.663, 55 of 83 severe lesions, while catastrophic confusion between mild and severe lesions stood at 0.41%, 4 of the 985 reference lesions. Conclusions: Coupling lesion-preserving tiling with ordinal severity-aware detection improves localization and severity-weighted precision by resolvable margins, shifts every severe-grade measure in a clinically preferable direction, and provides a reproducible reference protocol for comparing detector architectures on this task.

Kök kanal tedavisi, hastalıklı dokuları iyileştirmek veya sağlıklı periapikal dokuları korumak amacıyla günümüzde yaygın olarak kullanılan bir tedavi yöntemidir. Bu tedavinin sonucunda, dişteki madde kaybına bağlı olarak doğru koronal restorasyon seçimi yapılmalıdır. Güncel yaklaşımlar dijital uygulamalarla birlikte daha minimal invaziv tedavileri tercih olarak sunmaktadır. Koronal bölgede madde kaybının fazla olduğu dişlerde kök içerisinden destek alınırken, kalan sağlıklı diş dokusu kalınlığı ve miktarı sonucunda daha konservatif tedaviler yapılabilmektedir. Bu derlemenin amacı, kök kanal tedavisi görmüş dişlerin koronal restorasyonunda geçmişten günümüze gelen tedavi seçenekleri ve en güncel yaklaşımları değerlendirmektir.

Background/Objectives: The aim of this study was to compare the effectiveness of the Reciproc Blue (RB) and MicroMega Remover (MR) systems in removing root canal filling material and to evaluate the effect of passive ultrasonic irrigation (PUI) on remaining filling material (RFM) using micro-computed tomography (micro-CT)-based three-dimensional (3D) analysis. Methods: Forty single-rooted mandibular premolar teeth were included in the study. The root canals were prepared up to size F2 using the ProTaper Gold rotary file system and obturated with the lateral compaction technique. After the initial micro-CT scan, the teeth were randomly divided into four groups: Group RB, Group MR, Group RB + PUI, and Group MR + PUI (n = 10). Following retreatment, a second micro-CT scan was performed. The percentage of RFM was calculated, and statistical analyses were performed using Kruskal–Wallis and Mann–Whitney U tests with Bonferroni correction. A rank-based factorial analysis was additionally performed (p < 0.05). Results: RFM was observed in all groups. No significant difference was found between the RB (7.37%) and MR (7.31%) systems (p > 0.05). However, the groups treated with PUI (RB + PUI and MR + PUI) showed significantly lower RFM values than the groups without PUI (p = 0.001). Factorial analysis revealed no significant effect of file system or file system × PUI interaction, whereas PUI significantly reduced RFM (p < 0.001). Conclusions: The RB and MR systems demonstrated similar effectiveness in removing root canal filling material. Although complete canal cleanliness could not be achieved, under the in vitro conditions of the present study, PUI significantly reduced the amount of micro-CT-measured RFM.

This study retrospectively evaluated and compared the clinical and radiographic outcomes of full pulpotomy (FP) and root canal treatment (RCT) in teeth wit

Background/Objectives: The European Society of Endodontology names two filling requirements with a reproducible link to outcome: apical extent and material density. Panoramic radiography shows every treated tooth in one exposure, but systems built on it pool detection with grading into one average precision that hides which of the two has failed. This study graded both axes on panoramic radiographs with detection and discrimination measured separately. Methods: In this retrospective study, two specialist endodontists read 830 panoramic radiographs independently, and a third adjudicated every disagreement. The 1668 root fillings of 806 radiographs were encoded into a binary and an axis-resolved label space, and 160 radiographs were locked away before training. The remaining 646 were evaluated under five-fold cross-validation, with four architectural interventions tested alongside input resolution. Localisation was measured by class-agnostic recall, grading by matched-box accuracy and per-axis F1, and label spaces were compared by paired bootstrap. Results: Refining the label space left detection recall at 92.6% against 93.1%, but cost 11.41 points of matched-box accuracy, 8.20 to 14.46, and 12.16 on the locked test. Asked the same binary question, it fell 1.12 points behind in cross-validation and 3.13 points behind on the locked test, where the binary decision reached 76.25% sensitivity given detection and 68.75% specificity at the best of ten readings, or 71.52% and 68.21% at the development-selected one. At 1024 pixels, no architectural change improved density F1 beyond the 8.2 to 10.8 points this design can detect, and enlarging training from 207 to 517 radiographs gained 11.42, 5.92 to 16.56. Conclusions: Separating detection from grading locates the cost of a finer label space in discrimination, bounds four architectural interventions below the resolution of this design, and supports a binary but not an axis-resolved quality decision as a potential teaching and audit aid, pending external validation beyond this single centre, not a screening test.