ArticleEBioMedicine2020
Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Clinical Application of Deep Learning for Spine MRI Interpretation: A Multicenter Evaluation of Artificial-Intelligence-Assisted versus Manual Reading on Diagnostic Agreement with the Reference Standard.Research (Washington, D.C.) · 2026Article
- Global trends and developments in pulmonary magnetic resonance imaging research: a bibliometric analysis of the past decade.Quantitative imaging in medicine and surgery · 2025Article
- Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic.Soft computing · 2023Article
- A Short Review Comparing Carbon-Based Electrochemical Platforms With Other Materials For Biosensing SARS-Cov-2.ChemistrySelect · 2022Review
- An efficient deep neural network framework for COVID-19 lung infection segmentation.Information sciences · 2022Article
- Deep Learning-Enabled Clinically Applicable CT Planbox for Stroke With High Accuracy and Repeatability.Frontiers in neurology · 2022Article
- Detection and characterization of COVID-19 findings in chest CT: Feasibility and applicability of an AI-based software tool.Medicine · 2021Observational
- A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.European radiology · 2021Article
- Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology.Intelligent medicine · 2021Article
- AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.Applied soft computing · 2021Article
- CT scan AI-aided triage for patients with COVID-19 in China.The Lancet. Digital health · 2020Article
- Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19.Cellular and molecular bioengineering · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterstitial lung disease requires frequent re-examination, which directly causes excessive cumulative radiation exposure. To date, AI has not been applied to CT for enhancing clinical care; thus, we hypothesize AI may empower CT with intelligence to realize automatic and accurate pulmonary scanning, thus dramatically decrease medical radiation exposure without compromising patient care.
methodsFacial boundary detection was realized by recognizing adjacent jaw position through training and testing a region proposal network (RPN) on 76,882 human faces using a preinstalled 2-dimensional camera; the lung-fields was then segmented by V-Net on another training set with 314 subjects and calculated the moving distance of the scanning couch based on a pre-generated calibration table. A multi-cohort study, including 1,186 patients was used for validation and radiation dose quantification under three clinical scenarios.
findingsA U-HAPPY (United imaging Human Automatic Planbox for PulmonarY) scanning CT was designed. Error distance of RPN was 4·46±0·02 pixels with a success rate of 98·7% in training set and 2·23±0·10 pixels with 100% success rate in testing set. Average Dice's coefficient was 0·99 in training set and 0·96 in testing set. A calibration table with 1,344,000 matches was generated to support the linkage between camera and scanner. This real-time automation makes an accurate plan-box to cover exact location and area needed to scan, thus reducing amounts of radiation exposures significantly (all, P<0·001).
interpretationU-HAPPY CT designed for pulmonary imaging acquisition standardization is promising for reducing patient risk and optimizing public health expenditures.
fundingThe National Natural Science Foundation of China.
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