Evidence map›Paper›PMID 39875279›Full record

ArticleInternational dental journal2025

Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning.

Wei Li, Linlin Li, Wenchong Xu, Yuting Guo, Min Xu, Shengyuan Huang, Dong Dai, Chang Lu, Shuai Li, Jiang Lin

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In one paragraph

Article in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Wei LiDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Linlin LiDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Wenchong XuState Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
Yuting GuoState Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
Min XuDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Shengyuan HuangDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Dong DaiDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Chang LuDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Shuai LiState Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China. Electronic address: lishuai@buaa.edu.cn.
Jiang LinDepartment of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. Electronic address: kelvinperio@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION AND

aimThe assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators.

methodsThe study included the periodontal probing data and intraoral scan images of 120 patients with periodontitis. The deep learning model GC-U-Net was used to automatically segment and identify the gingival inflammation regions. The performance of the model was evaluated by the Dice coefficient (Dice), intersection over union (IoU), and pixel accuracy (PA), and the correlation between the identification performance and the periodontal examination index was analysed.

resultsThe GC-U-Net model showed high recognition accuracy, with a Dice of 77.8%, an IoU of 65.4%, and a PA of 93.7%. This model demonstrated a strong positive correlation with the sulcus bleeding index (SBI; r = 0.836, P < .001), a moderately strong positive correlation with the bleeding index (BI; r = 0.618, P < .001), and a negative correlation with the probe depth (PD; r = - 0.425, P < .001).

conclusionThe study has successfully developed an automatic identification method for surface characteristics of gingival inflammation based on deep learning and IOS technology, providing a standardised and automated auxiliary tool for clinical gingival inflammation examination with high accuracy and significant correlation with clinical indicators. CLINICAL RELEVANCE: This method can reduce subjective judgment in the clinical assessment of gingival inflammation, improve the consistency and reliability of identification, and play an important auxiliary role in clinical diagnosis and treatment planning.

Indexed as

Deep LearningGingivitisImage Processing, Computer-AssistedAdultAlgorithmsFemaleHumansMaleMiddle AgedPeriodontal IndexPeriodontitisArtificial intelligenceDeep learningGingival inflammationIntraoral scanningPeriodontitis

Identifiers

PMID39875279
PMCPMC12142751

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.