Evidence map›Paper›PMID 37646791›Full record

ArticleNeuroradiology2023

Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software.

Taku Tajima, Hiroyuki Akai, Koichiro Yasaka, Akira Kunimatsu, Naoki Yoshioka, Masaaki Akahane, Kuni Ohtomo, Osamu Abe, Shigeru Kiryu

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In one paragraph

Article in Neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 22% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Evaluation of Approved AI-based Brain Aneurysm Detection Software in Clinical Practice: Comparison with Radiologist Assessment and Image Re-review.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026
    Article
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  4. 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 at 4 institutions in 1 country.

Taku TajimaDepartment of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-Ku, Tokyo, 108-8329, Japan.
Hiroyuki AkaiDepartment of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
Koichiro YasakaDepartment of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
Akira KunimatsuDepartment of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-Ku, Tokyo, 108-8329, Japan.
Naoki YoshiokaDepartment of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
Masaaki AkahaneDepartment of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan.
Kuni OhtomoInternational University of Health and Welfare, 2600-1 Kitakanamaru, Otawara, Tochigi, 324-8501, Japan.
Osamu AbeDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan.
Shigeru KiryuDepartment of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan. kiryu-tky@umin.ac.jp.ORCID http://orcid.org/0000-0003-1440-9483
International University of Health and Welfare · JPMita Hospital · JPThe University of Tokyo · JPSystems Biology Institute · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo compare the diagnostic performance of 1.5 T versus 3 T magnetic resonance angiography (MRA) for detecting cerebral aneurysms with clinically available deep learning-based computer-assisted detection software (EIRL aneurysm® [EIRL_an]), which has been approved by the Japanese Pharmaceuticals and Medical Devices Agency. We also sought to analyze the causes of potential false positives.

methodsIn this single-center, retrospective study, we evaluated the MRA scans of 90 patients who underwent head MRA (1.5 T and 3 T in 45 patients each) in clinical practice. Overall, 51 patients had 70 aneurysms. We used MRI from a vendor not included in the dataset used to create the EIRL_an algorithm. Two radiologists determined the ground truth, the accuracy of the candidates noted by EIRL_an, and the causes of false positives. The sensitivity, number of false positives per case (FPs/case), and the causes of false positives were compared between 1.5 T and 3 T MRA. Pearson's χ

resultsThe sensitivity was high for 1.5 T and 3 T MRA (0.875‒1), but the number of FPs/case was significantly higher with 3 T MRA (1.511 vs. 2.578, p < 0.001). The most common causes of false positives (descending order) were the origin/bifurcation of vessels/branches, flow-related artifacts, and atherosclerosis and were similar between 1.5 T and 3 T MRA.

conclusionEIRL_an detected significantly more false-positive lesions with 3 T than with 1.5 T MRA in this external validation study. Our data may help physicians with limited experience with MRA to correctly diagnose aneurysms using EIRL_an.

Indexed as

Deep LearningIntracranial AneurysmComputersHumansMagnetic Resonance AngiographyRetrospective StudiesSoftwareComputer-assisted detectionDeep learningIntracranial aneurysmMagnetic resonance angiography

Identifiers

PMID37646791
OpenAlexW4386271916

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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.