Evidence map›Paper›PMID 39050240›Full record

ArticleAsia Oceania journal of nuclear medicine & biology2024

Development of an automated region-of-interest-setting method based on a deep neural network for brain perfusion single photon emission computed tomography quantification methods.

Taeko Tomimatsu, Kosuke Yamashita, Takumi Sakata, Ryosuke Kamezaki, Ryuji Ikeda, Shinya Shiraishi, Yoshikazu Uchiyama, Shigeki Ito

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In one paragraph

Article in Asia Oceania journal of nuclear medicine & biology, 2024. 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

8 authors.

Taeko TomimatsuGraduate School of Health Sciences, Kumamoto University, Japan.
Kosuke YamashitaGraduate School of Health Sciences, Kumamoto University, Japan.
Takumi SakataGraduate School of Health Sciences, Kumamoto University, Japan.
Ryosuke KamezakiDepartment of Central Radiology Kumamoto University Hospital, Japan.
Ryuji IkedaDepartment of Central Radiology Kumamoto University Hospital, Japan.
Shinya ShiraishiDepartment of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Japan.
Yoshikazu UchiyamaDepartment of Information and Communication Technology, Faculty of Engineering, University of Miyazaki, Japan.
Shigeki ItoDepartment of Medical Radiation Sciences, Faculty of Life Science, Kumamoto University, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: A simple noninvasive microsphere (SIMS) method using Methods: A U-Net architecture based on convolutional neural networks was used to determine the PA and AAo candidate regions. Images of 290 patients who underwent Results: The coincidence ratio for the locations of the PA and AAo-ROI obtained using the DCNN method and that for the manual method was 100%. Strong correlations were observed between the AUC counts using the DCNN and manual methods. Conclusion: New ROI- setting programs were developed using a deep convolution neural network DCNN to determine the input functions for the SIMS and IBUR methods. The accuracy of these methods is comparable to that of the manual method.

Indexed as

123I-IMP99mTc-ECDDCNNrCBF quantificationROI setting  A B S T R A C T

Identifiers

PMID39050240
PMCPMC11263778

What OpenQuestion holds

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Registered trials

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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.