Evidence map›Paper›PMID 39763696›Full record

ArticlePeerJ2025

The application of deep learning in early enamel demineralization detection.

Ketai He, Rongxiu Zhang, Muchun Liang, Keyue Tian, Kaihui Luo, Ruoshi Chen, Jianpeng Ren, Jiajun Wang, Juan Li

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PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
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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

9 authors.

Ketai He *State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Rongxiu Zhang *Department of Stomatology, The First Affiliated Hospital of Bengbu Medical University, Chengdu, China.
Muchun LiangState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Keyue TianState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Kaihui LuoState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Ruoshi ChenChengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China.
Jianpeng RenChengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China.
Jiajun WangCollege of Software, Sichuan University, Chengdu, China.
Juan LiState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces. Methods: A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN). The model was trained to automate the detection of enamel demineralization. Its performance was compared to two junior dentists' diagnostic abilities. Results: The model achieved an F1-score of 0.856 for detecting demineralized teeth on the validation set, a metric that reflects comprehensive diagnostic performance, demonstrating performance close to that of senior dentists. With the the model's assistance, the junior dentists' average F1-scores improved significantly-from 0.713 and 0.689 to 0.897 and 0.949, respectively ( Conclusion: Deep learning can accurately segment tooth surfaces and lesion contours, enhancing the precision, accuracy, and efficiency of enamel demineralization diagnosis and area delineation.

Indexed as

Deep LearningTooth DemineralizationAdolescentAdultDental EnamelFemaleHumansMaleNeural Networks, ComputerPhotography, DentalRetrospective StudiesYoung AdultArtificial intelligenceDeep learningDiagnostic imagingTooth demineralization

Identifiers

PMID39763696
PMCPMC11700490

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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.