Evidence map›Paper›PMID 39728913›Full record

ArticleTomography (Ann Arbor, Mich.)2024

Automated Measurement of Effective Radiation Dose by

Yujin Eom, Yong-Jin Park, Sumin Lee, Su-Jin Lee, Young-Sil An, Bok-Nam Park, Joon-Kee Yoon

Abstract read
In one paragraph

Article in Tomography (Ann Arbor, Mich.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. AI in Medical Imaging and Image Processing.Journal of clinical medicine · 2025
    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

7 authors.

Yujin EomDepartment of AI Mobility Engineering, Ajou University, Suwon 16499, Republic of Korea.ORCID 0009-0000-7077-2303
Yong-Jin ParkDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Sumin LeeDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Su-Jin LeeDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Young-Sil AnDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Bok-Nam ParkDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Joon-Kee YoonDepartment of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon 16499, Republic of Korea.ORCID 0000-0001-9934-0125

Funding

Korea Health Industry Development Institute Korea Government Grant Program for Education and Research in Medical AIMinistry of Science and ICT NRF-2022R1F1A1076500
6 · The paper itself

Abstract

BACKGROUND/

objectivesCalculating the radiation dose from CT in

methodsThe torso CT was segmented into six distinct regions using TotalSegmentator. An automated program was employed to extract the necessary information and calculate the effective dose (ED) of PET/CT. The accuracy of our automated program was verified by comparing the EDs calculated by the program with those determined by a nuclear medicine physician (n = 30). Additionally, we compared the EDs obtained from an older PET/CT scanner with those from a newer PET/CT scanner (n = 42).

resultsThe CT ED calculated by the automated program was not significantly different from that calculated by the nuclear medicine physician (3.67 ± 0.61 mSv and 3.62 ± 0.60 mSv, respectively,

conclusionsWe successfully developed an automated program for calculating the ED of torso

Indexed as

Deep LearningFluorodeoxyglucose F18Positron Emission Tomography Computed TomographyRadiation DosageRadiopharmaceuticalsAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedFluorodeoxyglucose F18Radiopharmaceuticals18F-FDGcomputed tomographydeep learningeffective dosepositron emission tomography

Identifiers

PMID39728913
PMCPMC11679132

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.