Oral surgery, oral medicine, oral pathology and oral radiology · 2026
Fractal analysis as a tool for distinguishing periradicular rarefactions in endodontically diseased teeth: a diagnostic study.
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Saricam Esma, Altunkaynak Bulent, Kayaoglu Guven
Аннотация
AIM: This study evaluated whether fractal analysis of periapical radiographs can detect periradicular disease.
MATERIALS AND METHODS: Sixty periapical radiographs of mandibular molars were divided into four groups based on the Periapical Index (PAI) (n = 15 per group; scores 1-4). Fractal analysis of periapical and furcation regions was performed using the box-counting method in ImageJ. Multiple region of interest (ROI) sizes were tested (20 × 20, 25 × 25, 30 × 30, 40 × 40, 50 × 50, 60 × 60, 70 × 70, 20 × 50, and 20 × 40 pixels) to determine the best correlation with PAI scores. Statistical analyses included one-way analysis of variance with Tukey's post hoc test, Student t-test, and ROC analysis (p < 0.05). External validation was conducted using 33 additional radiographs.
RESULTS: Fractal dimension (FD) values from periapical ROIs (20 × 20, 25 × 25, and 30 × 30) and bifurcation ROIs (20 × 20 and 25 × 25) significantly differed between healthy (PAI 1-2) and diseased (PAI 3-4) teeth (P < .05). ROC analysis supported the diagnostic value of these ROIs, with the highest AUC for the bifurcation 20 × 20 ROI. In the internal dataset, periapical and bifurcation 20 × 20 ROIs achieved the highest accuracies (0.82 and 0.80, respectively), and the bifurcation 20 × 20 ROI showed the greatest sensitivity (0.93). External validation confirmed the robustness of the bifurcation 20 × 20 ROI (accuracy 0.76, sensitivity 0.65, and specificity 0.88).
CONCLUSION: Fractal analysis can identify periradicular disease on periapical radiographs. Diagnostic accuracy depends on the analyzed region and ROI size, with the 20 × 20-pixel bifurcation ROI yielding the best performance.
MATERIALS AND METHODS: Sixty periapical radiographs of mandibular molars were divided into four groups based on the Periapical Index (PAI) (n = 15 per group; scores 1-4). Fractal analysis of periapical and furcation regions was performed using the box-counting method in ImageJ. Multiple region of interest (ROI) sizes were tested (20 × 20, 25 × 25, 30 × 30, 40 × 40, 50 × 50, 60 × 60, 70 × 70, 20 × 50, and 20 × 40 pixels) to determine the best correlation with PAI scores. Statistical analyses included one-way analysis of variance with Tukey's post hoc test, Student t-test, and ROC analysis (p < 0.05). External validation was conducted using 33 additional radiographs.
RESULTS: Fractal dimension (FD) values from periapical ROIs (20 × 20, 25 × 25, and 30 × 30) and bifurcation ROIs (20 × 20 and 25 × 25) significantly differed between healthy (PAI 1-2) and diseased (PAI 3-4) teeth (P < .05). ROC analysis supported the diagnostic value of these ROIs, with the highest AUC for the bifurcation 20 × 20 ROI. In the internal dataset, periapical and bifurcation 20 × 20 ROIs achieved the highest accuracies (0.82 and 0.80, respectively), and the bifurcation 20 × 20 ROI showed the greatest sensitivity (0.93). External validation confirmed the robustness of the bifurcation 20 × 20 ROI (accuracy 0.76, sensitivity 0.65, and specificity 0.88).
CONCLUSION: Fractal analysis can identify periradicular disease on periapical radiographs. Diagnostic accuracy depends on the analyzed region and ROI size, with the 20 × 20-pixel bifurcation ROI yielding the best performance.