ArticleRadiology. Artificial intelligence2021
Automatic Scan Range Delimitation in Chest CT Using Deep Learning.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- TOPOS: target organ prediction on scout views for automated CT scan planning.European radiology · 2026Article
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Mapping the Landscape of Over-Scanning in CT Imaging: A Scoping Review.Journal of medical radiation sciences · 2026Review
- AI-driven assessment of over-scanning in chest CT: A systematic review and meta-analysis.European journal of radiology open · 2025Review
- Detecting the left atrial appendage in CT localizers using deep learning.Scientific reports · 2025Article
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- [Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography].Radiologie (Heidelberg, Germany) · 2024Review
- Determining body height and weight from thoracic and abdominal CT localizers in pediatric and young adult patients using deep learning.Scientific reports · 2023Article
- Enhancing Radiation Dose Efficiency in Prospective ECG-Triggered Coronary CT Angiography Using Calcium-Scoring CT.Diagnostics (Basel, Switzerland) · 2023Article
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective.PET clinics · 2022Review
- Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging.Insights into imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and evaluate fully automatic scan range delimitation for chest CT by using deep learning. MATERIALS AND
methodsFor this retrospective study, scan ranges were annotated by two expert radiologists in consensus in 1149 (mean age, 65 years ± 16 [standard deviation]; 595 male patients) chest CT topograms acquired between March 2002 and February 2019 (350 with pleural effusion, 376 with atelectasis, 409 with neither, 14 with both). A conditional generative adversarial neural network was trained on 1000 randomly selected topograms to generate virtual scan range delimitations. On the remaining 149 topograms the software-based scan delimitations, scan lengths, and estimated radiation exposure were compared with those from clinical routine. For statistical analysis an equivalence test (two one-sided
resultsThe software-based scan ranges were similar to the radiologists' annotations, with a mean Dice score coefficient of 0.99 ± 0.01 and an absolute difference of 1.8 mm ± 1.9 and 3.3 mm ± 5.6 at the upper and lower boundary, respectively. An equivalence test indicated that both scan range delimitations were similar (
conclusionA conditional generative adversarial neural network was capable of automating scan range delimitation with high accuracy, potentially leading to shorter scan times and reduced radiation exposure.
Identifiers
What OpenQuestion holds
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.