Evidence map›Paper›PMID 31535278›Full record

ArticleOral radiology2020

Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography.

Motoki Fukuda, Kyoko Inamoto, Naoki Shibata, Yoshiko Ariji, Yudai Yanashita, Shota Kutsuna, Kazuhiko Nakata, Akitoshi Katsumata, Hiroshi Fujita, Eiichiro Ariji

Abstract read
PubMed Publisher
In one paragraph

Article in Oral radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 127 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
127citing papers in PubMed, 4 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

127 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Article
  7. Artificial Intelligence in Endodontics-A Bibliometric Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026
    Review
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Review
  17. Artificial intelligence model for application in dental traumatology.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2025
    Article
  18. Review
  19. Article
  20. Article

67 more citing papers are in PubMed but not listed here.

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.

Motoki Fukuda *Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. halpop@dpc.agu.ac.jp.ORCID http://orcid.org/0000-0002-0285-5008
Kyoko Inamoto *Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Naoki ShibataDepartment of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Yoshiko ArijiDepartment of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Yudai YanashitaDepartment of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Shota KutsunaDepartment of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Kazuhiko NakataDepartment of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Akitoshi KatsumataDepartment of Oral Radiology, Asahi University, Mizuho, Japan.
Hiroshi FujitaDepartment of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Eiichiro ArijiDepartment of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.

methodsThree hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.

resultsOf the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.

conclusionsThe CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.

Indexed as

Tooth FracturesArtificial IntelligenceCone-Beam Computed TomographyHumansRadiography, PanoramicReproducibility of ResultsTooth RootArtificial intelligenceDeep learningObject detectionPanoramic radiographyVertical root fracture

Identifiers

PMID31535278

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.