Evidence map›Paper›PMID 41286703›Full record

ArticleBMC medical imaging2025

Detection of intracranial hemorrhage using ultralow-dose brain computed tomography with deep learning reconstruction versus conventional-dose computed tomography.

Chuluunbaatar Otgonbaatar, Hyunjung Kim, Pil-Hyun Jeon, Sang-Hyun Jeon, Sung-Jin Cha, Jae-Kyun Ryu, Hackjoon Shim, Sung Min Ko, Jin Woo Kim

Abstract readComparative Study
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. 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

9 authors.

Chuluunbaatar OtgonbaatarMedical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
Hyunjung KimDepartment of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, 20 Ilsan-ro, Wonju, Gangwon, 26426, Republic of Korea.
Pil-Hyun JeonDepartment of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, 20 Ilsan-ro, Wonju, Gangwon, 26426, Republic of Korea.
Sang-Hyun JeonDepartment of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, 20 Ilsan-ro, Wonju, Gangwon, 26426, Republic of Korea.
Sung-Jin ChaDepartment of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, 20 Ilsan-ro, Wonju, Gangwon, 26426, Republic of Korea.
Jae-Kyun RyuMedical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
Hackjoon ShimMedical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
Sung Min KoDepartment of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, 20 Ilsan-ro, Wonju, Gangwon, 26426, Republic of Korea.
Jin Woo KimDepartment of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, 20 Ilsan-ro, Wonju, Gangwon, 26426, Republic of Korea. sunny-cocktail@hanmail.net.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to evaluate the diagnostic performance, image quality, and radiation dose among ultralow-dose protocol with deep learning reconstruction (DLR), ultralow-dose computed tomography (CT) with iterative reconstruction (IR), and conventional-dose protocols for detecting intracranial hemorrhage.

methodsThis retrospective study enrolled 93 patients (median age: 67 years; interquartile range [IQR]: 59-76 years; 61 males). A conventional-dose CT was obtained using 120 kVp, 123-188 mA and IR. Follow-up ultralow-dose CT was obtained using 120 kVp, 50 mA with IR and DLR. Qualitative assessments and quantitative assessments were conducted. The diagnostic performance for detecting intracranial hemorrhage was assessed.

resultsAn approximately 84.0% reduction in median volume CT dose index was found in the ultralow-dose CT protocol (5.6 mGy) compared with conventional-dose CT (35.02 mGy). Ultralow-dose CT with DLR significantly (p < 0.001) reduced image noise, improved signal-to-nosie ratio, and contrast-to-tnoise ratio compared with ultralow-dose CT with IR and conventional-dose CT. Ultralow-dose CT with DLR resulted in higher sensitivity (99.3% vs. 98.6%) and specificity (97.5% vs. 97.5%) for detecting intracranial hemorrhage than ultralow-dose CT with IR.

conclusionUltralow-dose CT with DLR is not inferior to conventional-dose CT in terms of image quality and diagnostic performance for the detection of intracranial hemorrhage, while achieving an approximate 87.7% reduction in radiation dose.

Indexed as

Deep LearningIntracranial HemorrhagesRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedRadiation DosageRetrospective StudiesSensitivity and SpecificityBrainDeep learning reconstructionHaemorrhageUltralow-dose CT

Identifiers

PMID41286703
PMCPMC12751909

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LicenceCC BY-NC-ND
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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.