Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study
Aims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA).Methods and results: A total of 52...
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/df409b4865ef4c79be78aeebe29d5378 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:df409b4865ef4c79be78aeebe29d5378 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:df409b4865ef4c79be78aeebe29d53782021-11-05T14:42:46ZDiagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study2297-055X10.3389/fcvm.2021.707508https://doaj.org/article/df409b4865ef4c79be78aeebe29d53782021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.707508/fullhttps://doaj.org/toc/2297-055XAims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA).Methods and results: A total of 527 patients (33.0% female, mean age: 62.2 ± 10.2 years) with suspected coronary artery disease (CAD) who underwent CCTA and invasive coronary angiography (ICA) were enrolled from 27 hospitals from January 2016 to August 2019. Using ICA as a standard reference, the diagnostic accuracy of the DL algorithm in the detection of ≥50% stenosis was compared to that of expert readers. In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) and a significantly higher area under the curve (AUC) (0.83, p < 0.001). In the patient-based evaluation, the DL algorithm achieved a higher sensitivity (90.0%), NPV (52.2%) and AUC (0.81). Generalizability analysis of the DL algorithm was conducted by comparing its diagnostic performance in subgroups stratified by sex, age, geographic area and CT scanner type. The AUCs of the DL algorithm in the aforementioned subgroups ranged from 0.79 to 0.86 and from 0.75 to 0.93 in the vessel-based and patient-based evaluations, both without significant group differences (p > 0.05). The DL algorithm significantly reduced post-processing time (160 [IQR:139–192] seconds), in comparison to manual work (p < 0.001).Conclusions: The DL algorithm performed no inferior to expert readers in CAD diagnosis on CCTA and had good generalizability and time efficiency.Lixue XuYi HeNan LuoNing GuoMin HongXibin JiaZhenchang WangZhenghan YangFrontiers Media S.A.articlecoronary artery diseasecomputed tomographic angiographydeep learninginvasive coronary angiography (ICA)diagnostic testDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
coronary artery disease computed tomographic angiography deep learning invasive coronary angiography (ICA) diagnostic test Diseases of the circulatory (Cardiovascular) system RC666-701 |
| spellingShingle |
coronary artery disease computed tomographic angiography deep learning invasive coronary angiography (ICA) diagnostic test Diseases of the circulatory (Cardiovascular) system RC666-701 Lixue Xu Yi He Nan Luo Ning Guo Min Hong Xibin Jia Zhenchang Wang Zhenghan Yang Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study |
| description |
Aims: In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA).Methods and results: A total of 527 patients (33.0% female, mean age: 62.2 ± 10.2 years) with suspected coronary artery disease (CAD) who underwent CCTA and invasive coronary angiography (ICA) were enrolled from 27 hospitals from January 2016 to August 2019. Using ICA as a standard reference, the diagnostic accuracy of the DL algorithm in the detection of ≥50% stenosis was compared to that of expert readers. In the vessel-based evaluation, the DL algorithm had a higher sensitivity (65.7%) and negative predictive value (NPV) (78.8%) and a significantly higher area under the curve (AUC) (0.83, p < 0.001). In the patient-based evaluation, the DL algorithm achieved a higher sensitivity (90.0%), NPV (52.2%) and AUC (0.81). Generalizability analysis of the DL algorithm was conducted by comparing its diagnostic performance in subgroups stratified by sex, age, geographic area and CT scanner type. The AUCs of the DL algorithm in the aforementioned subgroups ranged from 0.79 to 0.86 and from 0.75 to 0.93 in the vessel-based and patient-based evaluations, both without significant group differences (p > 0.05). The DL algorithm significantly reduced post-processing time (160 [IQR:139–192] seconds), in comparison to manual work (p < 0.001).Conclusions: The DL algorithm performed no inferior to expert readers in CAD diagnosis on CCTA and had good generalizability and time efficiency. |
| format |
article |
| author |
Lixue Xu Yi He Nan Luo Ning Guo Min Hong Xibin Jia Zhenchang Wang Zhenghan Yang |
| author_facet |
Lixue Xu Yi He Nan Luo Ning Guo Min Hong Xibin Jia Zhenchang Wang Zhenghan Yang |
| author_sort |
Lixue Xu |
| title |
Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study |
| title_short |
Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study |
| title_full |
Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study |
| title_fullStr |
Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study |
| title_full_unstemmed |
Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study |
| title_sort |
diagnostic accuracy and generalizability of a deep learning-based fully automated algorithm for coronary artery stenosis detection on ccta: a multi-centre registry study |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/df409b4865ef4c79be78aeebe29d5378 |
| work_keys_str_mv |
AT lixuexu diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT yihe diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT nanluo diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT ningguo diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT minhong diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT xibinjia diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT zhenchangwang diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy AT zhenghanyang diagnosticaccuracyandgeneralizabilityofadeeplearningbasedfullyautomatedalgorithmforcoronaryarterystenosisdetectiononcctaamulticentreregistrystudy |
| _version_ |
1718444260186914816 |