Evidence map›Paper›PMID 41932097›Full record

ReviewInternational dental journal2026

Progress of Multimodal Imaging: Bridging Orthodontics to Comprehensive Oral Multidisciplinary Care.

Yuqian Feng, Yifan Jia, Linhe Lv, Xiaoxi Wei, Min Hu

Abstract readReview
In one paragraph

Review in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yuqian FengDepartment of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China.
Yifan JiaDepartment of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China.
Linhe LvDepartment of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China.
Xiaoxi WeiDepartment of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China. Electronic address: xiaoxiw@jlu.edu.cn.
Min HuDepartment of Orthodontics, Hospital of Stomatology, Jilin University, Changchun, China. Electronic address: humin@jlu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION AND

aimsFor decades, orthodontic diagnosis and treatment have primarily depended on physical models, imaging techniques, and three-dimensional (3D) imaging technologies. Nevertheless, single-modal data exhibits inherent limitations, including insufficient spatial resolution, lack of tissue specificity, and discontinuous spatiotemporal information. These drawbacks make it challenging to meet the increasingly high requirements for precision and comprehensiveness in modern clinical decision-making. Multimodal data fusion technology, by integrating diverse types of medical data, can fully leverage the unique advantages of each modal data while compensating for their respective deficiencies. This review aims to systematically elaborate on the application value and research progress of multimodal data fusion technology in orthodontics, and to provide insights into its future development directions.

methodsA review of the contemporary clinical literature, relating to multimodal data fusion technology, was undertaken using information obtained from the electronic databases PubMed and Web of Science.

resultsEight types of commonly used modal data in orthodontics were identified, each with distinct characteristics. Multimodal data fusion technologies have been widely applied in orthodontic diagnosis and treatment, demonstrating significant application value. Moreover, such technologies have made notable progress in promoting multidisciplinary collaborative treatment involving orthodontics, oral surgery, and prosthodontics. The core advantage of multimodal data fusion technology lies in constructing comprehensive analytical models integrated with multidimensional biomechanical parameters, which can provide quantitative tools for dynamic orthodontic treatment monitoring and outcome evaluation.

conclusionMultimodal data fusion technology holds remarkable clinical value in the field of orthodontics. It not only supports precise orthodontic treatment but also enhances interdisciplinary collaboration through standardized data platforms. To further advance efficient and precise oral healthcare, future efforts should focus on improving the fusion accuracy of multimodal data and integrating more types of dynamic data. CLINICAL RELEVANCE: The application of multimodal data fusion technology can effectively address the limitations of single-modal data in orthodontic practice, providing clinicians with more comprehensive and accurate diagnostic information. It facilitates dynamic treatment monitoring and precise outcome evaluation, thereby optimizing clinical decision-making. Additionally, the standardized data platform established by this technology promotes seamless collaboration between orthodontists, oral surgeons, prosthodontists, and other specialists, contributing to the improvement of overall oral healthcare quality.

Indexed as

Multimodal ImagingOrthodonticsHumansImaging, Three-DimensionalArtificial intelligenceDeep learningMultimodal imagingOrthodonticsPatient simulation

Identifiers

PMID41932097
PMCPMC13084668

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.