Evidence map›Paper›PMID 42337754›Full record

ArticleBMC oral health2026

Deep learning-based identification, classification and segmentation of infraosseous periodontal defects using a YOLOv8 neural network.

Xukai Gu, Petra Orosz, Kristof Somodi, Xinda Li, Lei Yang, Hong Yang, Christoph Ramseier, Daniel Palkovics

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Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xukai GuDepartment of Periodontology, Semmelweis University, Szentkirályi Street 47, Budapest, 1088, Hungary.
Petra OroszDepartment of Periodontology, Semmelweis University, Szentkirályi Street 47, Budapest, 1088, Hungary.
Kristof SomodiDepartment of Periodontology, Semmelweis University, Szentkirályi Street 47, Budapest, 1088, Hungary.
Xinda LiDepartment of Periodontology, Semmelweis University, Szentkirályi Street 47, Budapest, 1088, Hungary.
Lei YangDepartment of Orthopedics, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
Hong YangDepartment of Pharmacology (State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, 150081, China.
Christoph RamseierDepartment of Periodontology, University of Bern, Freiburgstrasse 7, Bern, 3010, Switzerland.
Daniel PalkovicsDepartment of Periodontology, Semmelweis University, Szentkirályi Street 47, Budapest, 1088, Hungary. palkovics.daniel@semmelweis.hu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe defect morphology determines single-tooth prognosis, surgical planning, and regenerative potential of intrabony defects. 2D radiographs are often compromised by superimposition of anatomical structures, leading to potential misinterpretation and diagnostic inaccuracies. The aim of this study was to develop YOLOv8 model to identify, classify and segment periodontal defects.

methodsA dataset of 329 periapical radiographs from the Department of Periodontology, Semmelweis University, was utilized. All radiographs were preprocessed and manually annotated. The YOLOv8 neural network was trained and mainly assessed by area under the receiver operating characteristic curve (AUC-ROC), macro-average F1-score, Intersection over Union (IoU) and Dice similarity coefficient (DSC).

resultsThe model achieved an AUC-ROC of 0.8078 (95% CI: 0.6633-0.9257). The macro-average F1-score was found to be 0.6559. IoU and DSC value averaged 0.6881 ± 0.2398 and 0.7789 ± 0.2664. High spatial overlap was observed for one -wall (DSC: 0.8688), three-wall (DSC: 0.8723) and four-wall (DSC: 0.8641) defects.

conclusionsThe utilized YOLOv8 model demonstrated the capability to identify, classify, and segment infraosseous periodontal defects, achieving good discriminative power and moderate classification/ segmentation performance. Future work will have to focus on constructing a larger and even more refined training dataset to further enhance model performance.

Indexed as

Deep LearningNeural Networks, ComputerPeriodontal DiseasesDetection AlgorithmsHumansArtificial intelligenceDeep learningDental radiologyImage segmentationPeriapical radiographPeriodontal defect

Identifiers

PMID42337754
PMCPMC13576134

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