ArticleEuropean radiology2021
A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Automatic patient centering in computed tomography: a systematic review and meta-analysis.European radiology · 2025Pooled it
- [Applications of artificial intelligence in radiology].Radiologie (Heidelberg, Germany) · 2024Pooled it
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Patient Centring in CT: An International Survey of Clinical Practice and Future Directions.Journal of medical radiation sciences · 2026Article
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluation of the reliability of novel pelvic X-ray assessment software: CalculOrther.Acta orthopaedica et traumatologica turcica · 2026Article
- Quantifying Patient Positioning Errors and Radiation Dose Variation in Cardiac Computed Tomography Angiography.Journal of the Saudi Heart Association · 2026Article
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Role of Artificial Intelligence in Reducing Error Rates in Radiology: A Scoping Review.Cureus · 2025Review
- Future Applications of Cardiothoracic CT.Radiology · 2025Review
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- AI-based automatic patient positioning in a digital-BGO PET/CT scanner: efficacy and impact.EJNMMI physics · 2025Article
- Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.Radiology · 2025Review
- [Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].Radiologie (Heidelberg, Germany) · 2024Review
- Enhancing Radiation Dose Efficiency in Prospective ECG-Triggered Coronary CT Angiography Using Calcium-Scoring CT.Diagnostics (Basel, Switzerland) · 2023Article
- Review
- Assessing the Level of Understanding (Knowledge) and Awareness of Diagnostic Imaging Students in Ghana on Artificial Intelligence and Its Applications in Medical Imaging.Radiology research and practice · 2023Article
- Artificial Intelligence in Emergency Radiology: Where Are We Going?Diagnostics (Basel, Switzerland) · 2022Review
- Automatic Exposure Control Attains Radiation Dose Modulation Matched with the Head Size in Pediatric Brain CT.Tomography (Ann Arbor, Mich.) · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. MATERIALS AND
methodsOne hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups.
resultsThe AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas.
conclusionThe AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. KEY POINTS: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.
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Registered trials
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.