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The goal of this clinical trail is to evaluate the effectiveness and accuracy of the CCTA image assisted triage software(DeepVessel® Cardisight, Keya Medical.) for the triage of patients with suspected coronary artery disease.
The CCTA images collected by each center within a certain period of time would be screened, desensitized, and then evaluated by the software and independent expert group respectively, to evaluate the effectiveness and accuracy of the coronary CT angiography image stenosis assisted triage software developed by Koyal Medical Technology Co.
Experiment group: Evaluated by the software Independent expert group: Evaluated by experts (≥5 years of CCTA experience required)
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Suspected patients with coronary heart disease. | The coronary CT angiography (CCTA) images collected by each center within a certain period of time will be desensitized after the screening is successful. The final CCTA images were sent to an independent judgment expert group for diagnosis, the results of the test group and the independent judgment expert group were compared, and the clinical application of the coronary artery CT angiography image vascular stenosis auxiliary triage software developed by Keya Medical Technology Co., Ltd. was evaluated. Validity and Accuracy |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention | Other | Due to observational study |
|
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity and specificity of experiment group in triage for patients with suspected coronary artery disease | Based on the results of the independent expert group, uses stenosis degree≥50% as the boundary for triage indication on a per-patient basis. | October 13,2022 to March 1,2023 |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic time | Independent expert group: recording the sum of image post-processing time (T1) and the time to produce the results (T2); Experimental group: recording the time from image post-processing to outputting results; Calculate the difference between the two groups. | October 13,2022 to March 1,2023 |
| Measure | Description | Time Frame |
|---|---|---|
| Device Defects | In the course of clinical trials, there are unreasonable risks that may endanger human health and life safety under normal use of medical devices, such as label errors, quality problems, and frequent failures | October 13,2022 to March 1,2023 |
Inclusion Criteria:
CCTA images acquired by CT detectors need to meet the following requirements:
CCTA image quality score ≥ 3 (5-point Likert scale).
Exclusion Criteria:
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Suspected patients with coronary heart disease.
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| Name | Affiliation | Role |
|---|---|---|
| Bin Lu | Chinese Academy of Medical Sciences, Fuwai Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fuwai Hospital, Chinese Academy of Medical Sciences | Beijing | Beijing Municipality | China | |||
| The Pearl River Hospital of Southern Medical University |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 19095130 | Result | Meijboom WB, Meijs MF, Schuijf JD, Cramer MJ, Mollet NR, van Mieghem CA, Nieman K, van Werkhoven JM, Pundziute G, Weustink AC, de Vos AM, Pugliese F, Rensing B, Jukema JW, Bax JJ, Prokop M, Doevendans PA, Hunink MG, Krestin GP, de Feyter PJ. Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study. J Am Coll Cardiol. 2008 Dec 16;52(25):2135-44. doi: 10.1016/j.jacc.2008.08.058. | |
| 25122171 |
| Label | URL |
|---|---|
| Guerbet. Xenetix® 350: Comparative Assessment of Image Quality for Coronary CT Angiography (X-ACT)\[EB/OL\] | View source |
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| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| ID | Term |
|---|---|
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D014652 | Vascular Diseases |
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| Patient as research unit |
Evaluation criteria were established based on the results of the independent expert group to evaluate the total conformity rate, KAPPA value, positive predictive value (PPV) and negative predictive value (NPV) of triage tips for patients with suspected coronary heart disease in the experimental group. |
| October 13,2022 to March 1,2023 |
| Blood vessels were used as the research unit | Evaluation criteria were established based on the results of the independent expert group to evaluate the total conformity rate, KAPPA value, positive predictive value (PPV) and negative predictive value (NPV) of triage tips for patients with suspected coronary heart disease in the experimental group. | October 13,2022 to March 1,2023 |
| The vascular segment was used as the research unit | Evaluation criteria were established based on the results of the independent expert group to evaluate the total conformity rate, KAPPA value, positive predictive value (PPV) and negative predictive value (NPV) of triage tips for patients with suspected coronary heart disease in the experimental group. | October 13,2022 to March 1,2023 |
| Gender stratified statistics, respectively, with patients and blood vessels as research units | Evaluation criteria were established based on the results of the independent expert group to evaluate the sensitivity and specificity of the trial group to the triage hints of suspected patients with coronary heart disease | October 13,2022 to March 1,2023 |
| Manufacturer stratified statistics, respectively, with patients and blood vessels as research units | Evaluation criteria were established based on the results of the independent expert group to evaluate the sensitivity and specificity of the trial group to the triage hints of suspected patients with coronary heart disease | October 13,2022 to March 1,2023 |
| Row number stratified statistics, respectively, with patients and blood vessels as research units | Evaluation criteria were established based on the results of the independent expert group to evaluate the sensitivity and specificity of the trial group to the triage hints of suspected patients with coronary heart disease | October 13,2022 to March 1,2023 |
| The stratified statistics of tube voltage were conducted with patients and blood vessels as research units | Evaluation criteria were established based on the results of the independent expert group to evaluate the sensitivity and specificity of the trial group to the triage hints of suspected patients with coronary heart disease | October 13,2022 to March 1,2023 |
| Layer thickness stratified statistics, patients and blood vessels were used as research units | Evaluation criteria were established based on the results of the independent expert group to evaluate the sensitivity and specificity of the trial group to the triage hints of suspected patients with coronary heart disease | October 13,2022 to March 1,2023 |
| The patients and blood vessels were selected as the research units | Evaluation criteria were established based on the results of the independent expert group to evaluate the sensitivity and specificity of the trial group to the triage hints of suspected patients with coronary heart disease | October 13,2022 to March 1,2023 |
| Post-processing image quality evaluation | Calculate the percentage of post-processing images with the image quality score of 2 or above in the total post-processing images, and the difference between the independent expert group and experimental group. | October 13,2022 to March 1,2023 |
| Software performance evaluation | From the function of use, ease of operation, stability to evaluate, divided into satisfactory, general, unsatisfactory three levels. | October 13,2022 to March 1,2023 |
| Guangzhou |
| Guangdong |
| China |
| Affiliated Hospital of Zunyi Medical University | Zunyi | Guizhou | China |
| The First Affiliated Hospital of Hebei Medical University | Shijiazhuang | Hebei | China |
| Huanggang Central Hospital | Huanggang | Hubei | China |
| Result |
| Lucke C, Foldyna B, Andres C, Boehmer-Lasthaus S, Grothoff M, Nitzsche S, Gutberlet M, Lehmkuhl L. Post-processing in cardiovascular computed tomography: performance of a client server solution versus a stand-alone solution. Rofo. 2014 Dec;186(12):1111-21. doi: 10.1055/s-0034-1366726. Epub 2014 Aug 14. |
| 34127407 | Result | Choi AD, Marques H, Kumar V, Griffin WF, Rahban H, Karlsberg RP, Zeman RK, Katz RJ, Earls JP. CT Evaluation by Artificial Intelligence for Atherosclerosis, Stenosis and Vascular Morphology (CLARIFY): A Multi-center, international study. J Cardiovasc Comput Tomogr. 2021 Nov-Dec;15(6):470-476. doi: 10.1016/j.jcct.2021.05.004. Epub 2021 Jun 12. |
| 35090845 | Result | Paul JF, Rohnean A, Giroussens H, Pressat-Laffouilhere T, Wong T. Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection. Diagn Interv Imaging. 2022 Jun;103(6):316-323. doi: 10.1016/j.diii.2022.01.004. Epub 2022 Jan 26. |
| 23735619 | Result | Meyer M, Schoepf UJ, Fink C, Goldenberg R, Apfaltrer P, Gruettner J, Vajcs D, Schoenberg SO, Henzler T. Diagnostic performance evaluation of a computer-aided simple triage system for coronary CT angiography in patients with intermediate risk for acute coronary syndrome. Acad Radiol. 2013 Aug;20(8):980-6. doi: 10.1016/j.acra.2013.02.014. Epub 2013 Jun 2. |
| 40156689 | Derived | Chen Y, Yu H, Fan B, Wang Y, Wen Z, Hou Z, Yu J, Wang H, Tang Z, Li N, Jiang P, Wang Y, Yin W, Lu B. Diagnostic performance of deep learning-based coronary computed tomography angiography in detecting coronary artery stenosis. Int J Cardiovasc Imaging. 2025 May;41(5):979-989. doi: 10.1007/s10554-025-03383-0. Epub 2025 Mar 29. |