Download This Paper
Preprints with The Lancet is a collaboration between The Lancet Group of journals and SSRN to facilitate the open sharing of preprints for early engagement, community comment, and collaboration. Preprints available here are not Lancet publications or necessarily under review with a Lancet journal. These preprints are early-stage research papers that have not been peer-reviewed. The usual SSRN checks and a Lancet-specific check for appropriateness and transparency have been applied. The findings should not be used for clinical or public health decision-making or presented without highlighting these facts. For more information, please see the FAQs.
Diagnostic Performance of Deep Learning-Based Coronary Computed Tomography–Angiography Automatic Reconstruction and Diagnosis System: Model Establishment and Clinical Validation
28 Pages Posted: 13 Jul 2020
More...Abstract
Background: We pioneered a Deep Learning-based Coronary computed tomography–angiography Automatic Reconstruction and Diagnosis System (D-CARDS) for optimizing the workflow of diagnosis in coronary artery disease. Its efficiency and performance in clinical settings were verified.
Method: In the model establishment stage, D-CARDS was trained with coronary computed tomography–angiography (CCTA) imaging data from 10,410 patients divided into training, tuning, and external validation test sets in a ratio of 7:2:1. A total of 685 patients were included in the study of clinical validation. 350 CCTA cases were collected for comparison of time efficiency. Another 335 CCTA cases were selected to reveal the diagnostic performance of D-CARDS with paired invasive coronary angiography (ICA) as the reference standard. Stenosis of 50% was considered to be obstructive and 70% or more to be significantly obstructive. The diagnostic performance of D-CARDS was evaluated as the receiver operating characteristic curve (ROC) and the corresponding area under the curve (AUC) at patient, vascular and segmental bases compared to both unilateral expert and arbitrated expert results.
Findings: The average time taken of CCTA procedure after scanning (post-processing and diagnostic reporting) by D-CARDS was decreased by 73.3% from an average of 16.1 min with the conventional approach to 4.3 min (p=0.000). A total of 335 patients with 1,222 vessels and 3,559 segments were included in the final comparison of diagnostic performance. D-CARDS showed greater sensitivity (89.3% and 72.4%) than arbitrated expert results(82.6% and 62.0%)for detecting stenosis at both the 50% and 70% thresholds on patient-base, whereas its specificities were lower on every bases. The AUC showed that its diagnostic performance was equivalent to that of either of a unilateral expert on patient-based analysis, although slightly inferior to the arbitrated expert results.
Interpretation: D-CARDS greatly improves the efficiency of CCTA procedure. Its diagnostic performance in detecting coronary stenosis is closer to that of an attending radiologist on patient-based analysis. The system can be used to optimize CCTA workflow.
Funding Statement: This study was funded by Beijing science and technology committee. (grant reference number, Z201100005620009).
Declaration of Interests: The authors declare no competing interests.
Ethics Approval Statement: The study was approved by the Institutional Review Board (IRB)/Ethics Committee. The work was conducted in a manner compliant with the Measures for the Ethical Review of Biomedical Research Involving Humans and was adherent to the tenets of the Declaration of Helsinki.
Keywords: artificial intelligence; cardiovascular disease; computed tomography angiography; diagnostic imaging
Suggested Citation: Suggested Citation