scholarly journals Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jung-Joon Cha ◽  
Tran Dinh Son ◽  
Jinyong Ha ◽  
Jung-Sun Kim ◽  
Sung-Jin Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Optical Coherence ◽  
Learning Approaches ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Sensitivity Specificity ◽  
Testing Group

AbstractMachine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. Training and testing groups were partitioned in the ratio of 5:1. The OCT-based machine learning-FFR was derived for the testing group and compared with wire-based FFR in terms of ischemia diagnosis (FFR ≤ 0.8). The OCT-based machine learning-FFR showed good correlation (r = 0.853, P < 0.001) with the wire-based FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the OCT-based machine learning-FFR for the testing group were 100%, 92.9%, 87.5%, 100%, and 95.2%, respectively. The OCT-based machine learning-FFR can be used to simultaneously acquire information on both image and functional modalities using one procedure, suggesting that it may provide optimized treatments for intermediate coronary artery stenosis.

scholarly journals Machine learning for predicting fractional flow reserve based on optical coherence tomography in intermediate coronary stenosis

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.J Cha ◽  
T.D Son ◽  
J Ha ◽  
J.S Kim ◽  
S.J Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Training Group ◽  
Funding Source ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Luminal Area ◽  
Testing Group

Abstract Background Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been previously investigated. The objective of the study was to evaluate a machine learning method to estimate FFR based on intravascular OCT images in intermediate coronary lesions. Methods Data from both OCT- and wire-based FFR methods were obtained for lesions of the left anterior descending artery in 125 patients. Based on the total number of lesions, training and testing groups were partitioned at a ratio of 5:1. For the training group, 36 features, including 16 clinical and lesion characteristics, and 21 OCT features, were used to model machine learning-FFR. machine learning-FFR values were then derived for the testing group and compared with wire-based FFR values in terms of a diagnosis of ischemia (FFR &lt;0.8). Results Clinical and lesion characteristics and OCT features between the training and testing groups were similar. During the machine learning modeling of the training group, six important features of machine learning-FFR were identified: minimal luminal area, percentage of the stenotic area, lesion length, proximal luminal area, pre-procedural platelet count, and hypertension. machine learning-FFR values showed a good correlation (r=0.853, P&lt;0.001) with wire-based FFR values (Figure 1A). The diagnostic power of an FFR value less than 0.8, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of machine learning-FFR values for the testing group were 85.7%, 100%, 100%, 77.8%, and 90.5%, respectively (Figure 1B). Additionally, OCT-based machine learning-FFR values showed a good diagnostic accuracy compared with other image-based FFR values. Conclusions The OCT-based machine learning-FFR method can be used to simultaneously acquire information on both image and functional modalities using one invasive procedure, suggesting that it may be used to optimize treatments for intermediate coronary artery stenosis, as well as save time and cost. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Funded by the Korean government (MSIT) (no. 2017R1A2B2003191)

Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes

2021 ◽  
pp. 028418512098397
Author(s):  
Yang Li ◽  
Hong Qiu ◽  
Zhihui Hou ◽  
Jianfeng Zheng ◽  
Jianan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unstable Angina ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Flow Reserve ◽  
Sensitivity Specificity ◽  
Tomographic Angiography

Background Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR). Purpose To examine the ability of a DL-based CT-FFR in detecting hemodynamic changes of stenosis. Material and Methods This study included 73 patients (85 vessels) who were suspected of coronary artery disease (CAD) and received CCTA followed by invasive FFR measurements within 90 days. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristics curve (AUC) were compared between CT-FFR and CCTA. Thirty-nine patients who received drug therapy instead of revascularization were followed for up to 31 months. Major adverse cardiac events (MACE), unstable angina, and rehospitalization were evaluated and compared between the study groups. Results At the patient level, CT-FFR achieved 90.4%, 93.6%, 88.1%, 85.3%, and 94.9% in accuracy, sensitivity, specificity, PPV, and NPV, respectively. At the vessel level, CT-FFR achieved 91.8%, 93.9%, 90.4%, 86.1%, and 95.9%, respectively. CT-FFR exceeded CCTA in these measurements at both levels. The vessel-level AUC for CT-FFR also outperformed that for CCTA (0.957 vs. 0.599, P < 0.0001). Patients with CT-FFR ≤0.8 had higher rates of rehospitalization (hazard ratio [HR] 4.51, 95% confidence interval [CI] 1.08–18.9) and MACE (HR 7.26, 95% CI 0.88–59.8), as well as a lower rate of unstable angina (HR 0.46, 95% CI 0.07–2.91). Conclusion CT-FFR is superior to conventional CCTA in differentiating functional myocardial ischemia. In addition, it has the potential to differentiate prognoses of patients with CAD.

scholarly journals Functional assessment of tandem coronary artery stenosis by intracoronary optical coherence tomography-derived virtual fractional flow reserve: a case series

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Yoshiyuki Okuya ◽  
Fumiyasu Seike ◽  
Kohei Yoneda ◽  
Takefumi Takahashi ◽  
Koichi Kishi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Fractional Flow Reserve ◽  
Optical Coherence ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Proximal Lesion ◽  
A Value ◽  
The Wire ◽  
Tandem Lesions ◽  
Distal Site

Abstract Background Optical coherence tomography (OCT)-derived fractional flow reserve (FFR)—which may be calculated using fluid dynamics—demonstrated an excellent correlation with the wire-based FFR. However, the applicability of the OCT-derived FFR in the assessment of tandem lesions is currently unclear. Case summary We present two cases of tandem lesions in the mid segment of the left anterior descending (LAD) artery which could have assessed accurately by OCT-derived FFR. The first patient underwent wire-based FFR at the far distal site of LAD, showed a value of 0.66. The OCT-derived FFR was calculated, yielding a value of 0.64. In the absence of stenosis at the proximal lesion, the OCT-derived FFR was calculated as 0.79, which was as same as the wire-based FFR obtained after stenting to the proximal lesion. Thus, additional stenting was performed at the distal lesion. The second patient underwent wire-based FFR at the far distal site of LAD, showed a value of 0.76 which was as same vale as OCT-derived FFR. Considering the absence of stenosis in the proximal lesion, the OCT-derived FFR was estimated as 0.88. After coronary stenting in the proximal lesion, the wire-based FFR yielded a value of 0.90. Therefore, additional intervention to the distal lesion was deferred. Discussion The described reports are the first two cases which performed physiological assessment using OCT in tandem lesions. The OCT-derived FFR might be able to estimate the wire-based FFR and the severity of each individual lesion in patients with tandem lesions.

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