Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review
Background: The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFRCT), shortens the time of diagnosis of ischemia considerably and eliminates unnecessary invasive procedures, when compared to invasive coronary angiography with i...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | International Journal of Cardiology: Heart & Vasculature |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352906724001945 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850065654316531712 |
|---|---|
| author | Danny van Noort Liang Guo Shuang Leng Luming Shi Ru-San Tan Lynette Teo Min Sen Yew Lohendran Baskaran Ping Chai Felix Keng Mark Chan Terrance Chua Swee Yaw Tan Liang Zhong |
| author_facet | Danny van Noort Liang Guo Shuang Leng Luming Shi Ru-San Tan Lynette Teo Min Sen Yew Lohendran Baskaran Ping Chai Felix Keng Mark Chan Terrance Chua Swee Yaw Tan Liang Zhong |
| author_sort | Danny van Noort |
| collection | DOAJ |
| description | Background: The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFRCT), shortens the time of diagnosis of ischemia considerably and eliminates unnecessary invasive procedures, when compared to invasive coronary angiography with invasive FFR (iFFR). This systematic review aims to summarize the current evidence on the diagnostic accuracy of (ML-FFRCT) compared with iFFR for diagnosis of patient- and vessel-level coronary ischemia. Methods: To identify suitable studies, comprehensive literature search was performed in PubMed, the Cochrane Library, Embase, up to August 2023. The index test was ML derived FFR and studies with diagnostic test accuracy data of ML-FFRCT at a threshold of 0.8 were included for the review and meta-analysis. Quality of evidence was assessed using QUADAS-2 checklist. Results: After full text review of 230 identified studies, 17 were included for analysis, which encompassed 3255 participants (age 62.0 ± 3.7). 8 studies reported patient-level data; and 12, vessel-level data. With iFFR as the reference standard, the pooled patient-level sensitivity, specificity, and area-under-curve (AUC) of ML-FFRCT were 0.86 [95 % CI: 0.79, 0.91], 0.87 [95 % CI: 0.76, 0.94], and 0.92 [95 % CI: 0.89–0.94], respectively; and pooled vessel-level sensitivity, specificity, and AUC, 0.80 [95 % CI: 0.74–0.84], 0.84 [95 % CI: 0.77–0.89), and 0.88 [95 % CI: 0.85–0.91], respectively. Conclusions: This systemic review demonstrated the favourable diagnostic performance of ML-FFRCT against standard iFFR, although heterogeneity exists, providing support for the use of ML-FFRCT as a triage tool for non-invasive screening of coronary ischemia in the clinical setting. |
| format | Article |
| id | doaj-art-da8fd6419332467d97e0467438c498ac |
| institution | DOAJ |
| issn | 2352-9067 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Cardiology: Heart & Vasculature |
| spelling | doaj-art-da8fd6419332467d97e0467438c498ac2025-08-20T02:48:57ZengElsevierInternational Journal of Cardiology: Heart & Vasculature2352-90672024-12-015510152810.1016/j.ijcha.2024.101528Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic reviewDanny van Noort0Liang Guo1Shuang Leng2Luming Shi3Ru-San Tan4Lynette Teo5Min Sen Yew6Lohendran Baskaran7Ping Chai8Felix Keng9Mark Chan10Terrance Chua11Swee Yaw Tan12Liang Zhong13NHRIS, National Heart Centre, Singapore; Corresponding author at: National Heart Centre Singapore, 5 Hospital Drive, 169609 Singapore.Singapore Clinical Research Institute, Consortium for Clinical Research and Innovation, Singapore; Cochrane, SingaporeNHRIS, National Heart Centre, SingaporeSingapore Clinical Research Institute, Consortium for Clinical Research and Innovation, Singapore; Cochrane, Singapore; Duke-NUS Medical School, SingaporeNHRIS, National Heart Centre, Singapore; Duke-NUS Medical School, SingaporeDepartment of Diagnostic Imaging, National University Hospital, Singapore; Yong Loo Lin School of Medicine, National University Hospital, SingaporeTan Tock Seng Hospital, SingaporeNHRIS, National Heart Centre, Singapore; Duke-NUS Medical School, SingaporeYong Loo Lin School of Medicine, National University Hospital, Singapore; Department of Cardiology, National University Hospital, SingaporeNHRIS, National Heart Centre, Singapore; Duke-NUS Medical School, SingaporeYong Loo Lin School of Medicine, National University Hospital, Singapore; Department of Cardiology, National University Hospital, SingaporeNHRIS, National Heart Centre, Singapore; Duke-NUS Medical School, SingaporeNHRIS, National Heart Centre, Singapore; Duke-NUS Medical School, SingaporeNHRIS, National Heart Centre, Singapore; Duke-NUS Medical School, SingaporeBackground: The use of machine learning (ML) based coronary computed tomography angiography (CCTA) derived fractional flow reserve (ML-FFRCT), shortens the time of diagnosis of ischemia considerably and eliminates unnecessary invasive procedures, when compared to invasive coronary angiography with invasive FFR (iFFR). This systematic review aims to summarize the current evidence on the diagnostic accuracy of (ML-FFRCT) compared with iFFR for diagnosis of patient- and vessel-level coronary ischemia. Methods: To identify suitable studies, comprehensive literature search was performed in PubMed, the Cochrane Library, Embase, up to August 2023. The index test was ML derived FFR and studies with diagnostic test accuracy data of ML-FFRCT at a threshold of 0.8 were included for the review and meta-analysis. Quality of evidence was assessed using QUADAS-2 checklist. Results: After full text review of 230 identified studies, 17 were included for analysis, which encompassed 3255 participants (age 62.0 ± 3.7). 8 studies reported patient-level data; and 12, vessel-level data. With iFFR as the reference standard, the pooled patient-level sensitivity, specificity, and area-under-curve (AUC) of ML-FFRCT were 0.86 [95 % CI: 0.79, 0.91], 0.87 [95 % CI: 0.76, 0.94], and 0.92 [95 % CI: 0.89–0.94], respectively; and pooled vessel-level sensitivity, specificity, and AUC, 0.80 [95 % CI: 0.74–0.84], 0.84 [95 % CI: 0.77–0.89), and 0.88 [95 % CI: 0.85–0.91], respectively. Conclusions: This systemic review demonstrated the favourable diagnostic performance of ML-FFRCT against standard iFFR, although heterogeneity exists, providing support for the use of ML-FFRCT as a triage tool for non-invasive screening of coronary ischemia in the clinical setting.http://www.sciencedirect.com/science/article/pii/S2352906724001945Machine learningCoronary computed tomography angiography (CCTA)Fractional flow reserve (FFR)Invasive coronary angiography (ICA) |
| spellingShingle | Danny van Noort Liang Guo Shuang Leng Luming Shi Ru-San Tan Lynette Teo Min Sen Yew Lohendran Baskaran Ping Chai Felix Keng Mark Chan Terrance Chua Swee Yaw Tan Liang Zhong Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review International Journal of Cardiology: Heart & Vasculature Machine learning Coronary computed tomography angiography (CCTA) Fractional flow reserve (FFR) Invasive coronary angiography (ICA) |
| title | Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review |
| title_full | Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review |
| title_fullStr | Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review |
| title_full_unstemmed | Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review |
| title_short | Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review |
| title_sort | evaluating machine learning accuracy in detecting significant coronary stenosis using ccta derived fractional flow reserve meta analysis and systematic review |
| topic | Machine learning Coronary computed tomography angiography (CCTA) Fractional flow reserve (FFR) Invasive coronary angiography (ICA) |
| url | http://www.sciencedirect.com/science/article/pii/S2352906724001945 |
| work_keys_str_mv | AT dannyvannoort evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT liangguo evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT shuangleng evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT lumingshi evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT rusantan evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT lynetteteo evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT minsenyew evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT lohendranbaskaran evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT pingchai evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT felixkeng evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT markchan evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT terrancechua evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT sweeyawtan evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview AT liangzhong evaluatingmachinelearningaccuracyindetectingsignificantcoronarystenosisusingcctaderivedfractionalflowreservemetaanalysisandsystematicreview |