Evidence map›Paper›PMID 40777160›Full record

ReviewEuropean journal of radiology open2025

AI-driven assessment of over-scanning in chest CT: A systematic review and meta-analysis.

Mo'men Bani-Ahmad, Andrew England, Laura McLaughlin, Marwan Alshipli, Kholoud Alzyoud, Yasser H Hadi, Mark McEntee

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in European journal of radiology open, 2025. 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. Review
  2. 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.

Mo'men Bani-AhmadThe Discipline of Medical Imaging and Radiation Therapy, University College Cork, College Road, Cork T12 K8AF, Ireland.
Andrew EnglandThe Discipline of Medical Imaging and Radiation Therapy, University College Cork, College Road, Cork T12 K8AF, Ireland.
Laura McLaughlinThe Discipline of Medical Imaging and Radiation Therapy, University College Cork, College Road, Cork T12 K8AF, Ireland.
Marwan AlshipliDepartment of Medical Imaging and Radiography, Aqaba University of Technology, Aqaba, Jordan.
Kholoud AlzyoudFaculty of Applied Medical Sciences, Department of Medical Imaging, The Hashemite University, Zarqa, Jordan.
Yasser H HadiThe Discipline of Medical Imaging and Radiation Therapy, University College Cork, College Road, Cork T12 K8AF, Ireland.
Mark McEnteeThe Discipline of Medical Imaging and Radiation Therapy, University College Cork, College Road, Cork T12 K8AF, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Scan range is crucial for CT acquisitions. However, irrelevant over-scanning in CT is common and contributes to a significant radiation dose. This review explores the role of artificial intelligence (AI) in addressing manual over-scanning in chest CT imaging. Methods: A systematic search of peer-reviewed publications was conducted between December 2015 and March 2025 in Embase, Scopus, Ovid, EBSCOhost, and PubMed. Two reviewers and an academic lecturer independently reviewed the articles to ensure adherence to inclusion criteria. The quality of the included studies was assessed using CLAIM and QUADAS-2 tools. Summary estimates on over-scanning at the upper and lower boundaries of the scan range in chest CT were derived using meta-analysis. Results: Five studies employed AI algorithms to assess manual over-scanning in chest CT using either 2D topograms or 3D axial images at low and standard doses. These models accurately determine the extent of over-scanning, demonstrating strong agreement with radiologist evaluations. All included studies revealed significant variation in over-scanning at the superior (13.5 mm) and inferior (30.2 mm) boundaries of the scan range (p < 0.001), with approximately two-thirds of the total over-scanning (43.2 mm) occurring at the inferior level (abdomen). Conclusions: Integrating AI tools into the over-scanning evaluation process may optimise chest CT imaging protocols and enhance patient safety by reducing over-scanning and radiation dose through real-time monitoring and retrospective analysis.

Indexed as

Anatomic coverageDeep learningMachine learningQuality controlScan range

Identifiers

PMID40777160
PMCPMC12329503

What OpenQuestion holds

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LicenceCC BY
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.