Evidence map›Paper›PMID 38321885›Full record

Observational studyThe Journal of international medical research2024

Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images.

Yuhei Takeshita, Shiro Onozawa, Shichiro Katase, Yuya Shirakawa, Kouji Yamashita, Jun Shudo, Akihito Nakanishi, Sadato Akahori, Kenichi Yokoyama

Erratum issuedAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in The Journal of international medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Yuhei TakeshitaDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.ORCID 0000-0002-0391-0072
Shiro OnozawaDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.
Shichiro KataseDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.
Yuya ShirakawaDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.
Kouji YamashitaDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.
Jun ShudoDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.
Akihito NakanishiDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.
Sadato AkahoriFUJIFILM Corporation, 2-26-30 Nishiazabu, Minato-ku, Tokyo, Japan.
Kenichi YokoyamaDepartment of Radiology, Kyorin University School of Medicine, 6-20-2 Shimorenjaku, Mitaka-shi, Tokyo, Japan 181-8611 Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo apply image registration in the follow up of lung nodules and verify the feasibility of automatic tracking of lung nodules using an artificial intelligence (AI) method.

methodsFor this retrospective, observational study, patients with pulmonary nodules 5-30 mm in diameter on computed tomography (CT) and who had at least six months follow-up were identified. Two radiologists defined a 'correct' cuboid circumscribing each nodule which was used to judge the success/failure of nodule tracking. An AI algorithm was applied in which a U-net type neural network model was trained to predict the deformation vector field between two examinations. When the estimated position was within a defined cuboid, the AI algorithm was judged a success.

resultsIn total, 49 lung nodules in 40 patients, with a total of 368 follow-up CT examinations were examined. The success rate for each time evaluation was 94% (345/368) and for 'nodule-by-nodule evaluation' was 78% (38/49). Reasons for a decrease in success rate were related to small nodules and those that decreased in size.

conclusionAutomatic tracking of lung nodules is highly feasible.

Indexed as

Lung NeoplasmsSolitary Pulmonary NoduleAlgorithmsArtificial IntelligenceHumansRetrospective StudiesTomography, X-Ray ComputedArtificial IntelligenceComputer-AssistedDeep LearningFollow-UpPulmonary nodules

Identifiers

PMID38321885
PMCPMC10851735

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.