Detection of periapical lesions in teeth with fixed prostheses using segmentation models and analyzing crown–lesion relationships

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dc.contributor.authorGul, Buse Cebi
dc.contributor.authorCebi, Can
dc.contributor.authorMalkoc, Ersel
dc.contributor.authorAltinsoy, Burcin
dc.contributor.authorBahce, Erkan
dc.contributor.authorKarabas, Ayse
dc.date.accessioned2026-04-04T13:18:55Z
dc.date.available2026-04-04T13:18:55Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractStatement of problem: Periapical lesions in teeth with fixed prostheses often remain undiagnosed in routine panoramic radiographic evaluations, leading to delayed treatment and potential tooth loss. The complex anatomy of prosthetic restorations can mask early periapical pathology, and manual detection is time-consuming and subject to interobserver variability. A standardized automated system is not currently available for the simultaneous detection of periapical lesions and the analysis of their relationship with fixed prostheses in panoramic radiographs. Purpose: The purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence models based on the YOLO11 architecture for detecting periapical lesions in teeth with fixed prostheses on panoramic radiographs and to analyze crown–lesion relationships using automated algorithms. Material and methods: A total of 1686 annotations (1033 crowns, 653 periapical lesions) were manually labeled on 404 retrospectively selected panoramic radiographs obtained from patients at Inonu University Faculty of Dentistry between March 2024 and May 2025. Manual labeling was performed independently by 2 experienced observers using the Roboflow platform. The dataset was divided into 77% training (312 images), 13% validation (52 images), and 10% testing (40 images). Five YOLO11 segmentation variants were trained for 150 epochs. Model performance was evaluated using precision, recall, mAP50, and mAP50–95 metrics. Statistical analyses were performed using Python 3.9 with scikit-learn and scipy libraries (α=.05). Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated for diagnostic performance assessment. Results: The YOLO11l-seg model achieved the highest performance with mAP50 of 0.885, recall of 0.853, and precision of 0.847. While all models demonstrated high success in crown detection (mAP50: 0.975 to 0.980), YOLO11l-seg yielded the best results for periapical lesion detection (mAP50: 0.794). Crown–lesion relationship analysis revealed that 84.62% of lesions were associated with crowns, with mandibular crowns showing a 2.7 times higher lesion prevalence than maxillary crowns (52.24% against 19.05%, P<.001). Conclusions: YOLO11-based artificial intelligence models demonstrated high accuracy for detecting periapical lesions in teeth with fixed prostheses. The developed Python algorithm successfully analyzed crown–lesion relationships, providing quantitative data for clinical assessment. © 2025 Editorial Council for The Journal of Prosthetic Dentistry
dc.identifier.doi10.1016/j.prosdent.2025.11.045
dc.identifier.issn0022-3913
dc.identifier.pmid41421947
dc.identifier.scopus2-s2.0-105025567174
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.prosdent.2025.11.045
dc.identifier.urihttps://hdl.handle.net/11616/108017
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Inc.
dc.relation.ispartofJournal of Prosthetic Dentistry
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.titleDetection of periapical lesions in teeth with fixed prostheses using segmentation models and analyzing crown–lesion relationships
dc.typeArticle

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