Evidence map›Paper›PMID 41844654›Full record

ArticleScientific data2026

A multi-focus oral panoramic x-ray image dataset based on pixel-level annotations.

Juncheng Cui, Jiale Gu, Yatong Guan, Haiqing Xiao, Kaimin Liu, Wei Zhang, Siqiao Li, Chenyue Song, Yuzhou Zhu, Yongying Tan and 3 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

13 authors.

Juncheng CuiCentral South University of Forestry and Technology, Changsha, 410004, China.
Jiale GuCentral South University of Forestry and Technology, Changsha, 410004, China.
Yatong GuanCentral South University of Forestry and Technology, Changsha, 410004, China.
Haiqing Xiao *Central South University of Forestry and Technology, Changsha, 410004, China. 104606938@qq.com.
Kaimin Liu *Central South University of Forestry and Technology, Changsha, 410004, China. liukm0108@163.com.
Wei Zhang *Karolinska Institutet, Stockholm, 171 77, Sweden. wei.zhang.2@ki.se.
Siqiao LiHarbin Institute of Technology, Harbin, 150001, China.
Chenyue SongHarbin Institute of Technology, Harbin, 150001, China.
Yuzhou ZhuNanjing University of Information Science and Technology, Nanjing, 210044, China.ORCID http://orcid.org/0009-0004-7035-9884
Yongying TanNanjing University of Information Science and Technology, Nanjing, 210044, China.
Xiaojuan LiuChangsha Stomatological Hospital, Changsha, 410000, China.
Yuan TaiTaiyuan University of Technology, Taiyuan, 030024, China.
Wenhao JiangHarbin Institute of Technology, Harbin, 150001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dataset is a comprehensive, large-scale collection of panoramic X-ray images developed to advance research in dental artificial intelligence. It comprises 8,655 de-identified images and over 30,186 pixel-level annotations of lesion regions. These images were obtained from Changsha Stomatological Hospital with informed patient consent, and all personally identifiable information was removed. Annotation was performed manually by a team of 20 experienced dental imaging specialists using LabelMe software. The experts delineated individual tooth contours through polygonal annotations, targeting various common oral conditions. This rigorous process ensured high annotation precision and clinical reliability.The dataset supports multiple research applications, including tooth segmentation, lesion detection, and computer-aided diagnosis. Its effectiveness has been validated using several widely adopted deep learning models, demonstrating strong generalization capabilities and broad applicability.

Indexed as

Radiography, PanoramicDeep LearningHumansImage Processing, Computer-AssistedTooth

Identifiers

PMID41844654
PMCPMC13139533

What OpenQuestion holds

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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.