Evidence map›Paper›PMID 38941003›Full record

ArticleOral radiology2024

Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence model using a cyclic generative adversarial network.

Motoki Fukuda, Michihito Nozawa, Hironori Akiyama, Eiichiro Ariji, Yoshiko Ariji

Abstract read
PubMed Publisher
In one paragraph

Article in Oral radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Motoki FukudaDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, 1-5-17 Otemae, Chuo-Ku, Osaka, Japan. fukuda-m@cc.osaka-dent.ac.jp.ORCID 0000-0002-0285-5008
Michihito NozawaDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, 1-5-17 Otemae, Chuo-Ku, Osaka, Japan.
Hironori AkiyamaDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, 1-5-17 Otemae, Chuo-Ku, Osaka, Japan.
Eiichiro ArijiDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Aichi-Gakuin University, Nagoya, Japan.
Yoshiko ArijiDepartment of Oral Radiology, School of Dentistry, Osaka Dental University, 1-5-17 Otemae, Chuo-Ku, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe objective of this study was to enhance the visibility of soft tissues on cone-beam computed tomography (CBCT) using a CycleGAN network trained on CT images.

methodsTraining and evaluation of the CycleGAN were conducted using CT and CBCT images collected from Aichi Gakuin University (α facility) and Osaka Dental University (β facility). Synthesized images (sCBCT) output by the CycleGAN network were evaluated by comparing them with the original images (oCBCT) and CT images, and assessments were made using histogram analysis and human scoring of soft-tissue anatomical structures and cystic lesions.

resultsThe histogram analysis showed that on sCBCT, soft-tissue anatomical structures showed significant shifts in voxel intensity toward values resembling those on CT, with the mean values for all structures approaching those of CT and the specialists' visibility scores being significantly increased. However, improvement in the visibility of cystic lesions was limited.

conclusionsImage synthesis using CycleGAN significantly improved the visibility of soft tissue on CBCT, with this improvement being particularly notable from the submandibular region to the floor of the mouth. Although the effect on the visibility of cystic lesions was limited, there is potential for further improvement through refinement of the training method.

Indexed as

Cone-Beam Computed TomographyArtificial IntelligenceFemaleHumansMaleNeural Networks, ComputerRadiographic Image Interpretation, Computer-AssistedArtificial intelligenceCone-beam CTCycleGANDeep learningSoft tissue

Identifiers

PMID38941003

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.