While invasive coronary angiography (ICA) provides limited anatomical information on the coronary artery, the results often form the basis for the decision to perform percutaneous coronary intervention (PCI) [1]. This reliance on ICA results in undesired outcomes, such as unnecessary PCI for functionally insignificant lesions or improper delays in PCI for functionally significant lesions [1]. An alternative method, the fractional flow reserve (FFR), serves as a hemodynamic correlation criterion that enhances the benefits of revascularization, improves event-free survival, and reduces health costs [2]. Despite its advantages, the invasive nature of FFR measurement, its need for expensive equipment, and the potential complications it may cause to the coronary artery limit its routine use in clinical practice.

For patients with low or moderate risk coronary artery disease (CAD), noninvasive tests such as the anatomy-based coronary computed tomography angiography (CCTA) can be attempted prior to more invasive testing [3, 4]. While CCTA is considered the first-line approach [4], with strengths including a high sensitivity (87%–99%) and moderate specificity (61%–83%) [5], the relatively high false-positive rate may lead to an increase in the need for ICA. More concerning is CCTA’s inability to assess the physiological function of the coronary artery based on the severity of coronary anatomical stenosis alone.

A promising solution to these limitations is the noninvasive computed tomography angiography–derived FFR (CT-FFR) [6]. This method can assess lesion-specific ischemia via computational fluid dynamics (CFD) without requiring changes to the CCTA data collection protocol, additional imaging, or drugs [6]. Impressively, CT-FFR has demonstrated an overall accuracy of 85% sensitivity and 82% specificity in pinpointing lesion-specific ischemia [6]. To streamline the integration of CT-FFR into clinical workflows and improve diagnostic accuracy, new software and algorithms have been created. These innovations facilitate cost-effective CT-FFR analyses on a standard workstation, eliminating the need for unnecessary ICA.

Machine learning-based flow assessments using artificial intelligence algorithms have recently been introduced to perform CT-FFR analysis. Coenen et al. [7] and Qiao et al. [8] suggested a supervised learning approach that involved training with diverse features from different anatomies and degrees of CAD, utilizing reduced-order CFD to compute CT-FFR values. In this prospective study, we assessed the diagnostic characteristics of CT-FFR by employing new deep learning software specifically designed for coronary lesion-specific ischemia analysis. This novel software package consists of two components: the Coronary Scope, a deep learning tool for evaluating the physiological function of the coronary artery, and the Compute Unified Device Architecture (CUDA) accelerated CFD software, tailored for analyzing incompressible fluid flow equations. These clinical experiments were conducted to evaluate the ability of the CT-FFR to identify coronary ischemia at FFR thresholds of 0.80 and 0.75, providing insights into its effectiveness and potential applications in CAD diagnosis.

This prospective study revealed that the new CT-FFR deep learning software exhibits a strong direct correlation with FFR and is effective in diagnosing lesion-specific ischemia. Furthermore, we confirmed the efficacy of CT-FFR to detect coronary artery ischemia with stenosis ranging from 30%–90% prior to an ICA referral.

Building on the findings of our study, FFR has emerged as a critical reference for managing coronary artery stenosis, allowing physicians to determine whether revascularization or drug therapy alone is the best course of action. It’s worth noting that the gray area of ischemic stenosis recognized by FFR ranges between 0.75 and 0.80. The well-known DEFER (Deferral versus Performance of Percutaneous Coronary Intervention of Functionally Nonsignificant Coronary Stenosis) and DEFER-DES (Proper Fractional Flow Reserve Criteria for Intermediate Lesions in the Era of Drug-eluting Stent) studies used the lower limit of the gray area (0.75) for decision-making regarding lesion ischemia [11, 12]. Notably, the DEFER randomized controlled study found delayed PCI based on an FFR $\ge$0.75 was favorable at a 15 years of follow-up. Compared to drug therapy alone, PCI of such functionally insignificant stenosis was not advantageous, and even led to increased myocardial infarction [12]. Moreover, the DEFER-DES study found that unnecessary stent implantation can be avoided by postponing PCI based on an FFR $\ge$0.75 [11].

The well-known FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) and FAME 2 trials, which used the upper limit of the gray area (0.80) for FFR, brought interesting insights into the management of coronary artery stenosis [13, 14]. The FAME trial at one year of follow-up and the FAME 2 study at three years of follow-up reported that FFR-guided PCI reduced major cardiovascular events when the FFR was $\le$0.80, compared to angiography-guided PCI or drug therapy alone [13, 14]. However, the five-year outcomes of the FAME trial revealed no mortality benefit with invasive FFR-guided PCI for stable CAD [15]. Regardless of whether 0.75 or 0.80 was chosen as the FFR threshold for diagnosing ischemia, the clinical outcome observation for FFR-guided revascularization remained unaffected. This was due to a continuous and independent relationship between clinical outcomes and the FFR for drugs versus revascularization [12]. In this study, we specifically used FFR thresholds of 0.80 and 0.75 to measure the performance of the CT-FFR in detecting coronary ischemia.

CCTA is an established noninvasive modality increasingly used to detect suspicious CAD. However, its inability to assess the hemodynamic effects of lesions and a high false-positive rate result in an overall overestimation of coronary artery stenosis. Even when ICA confirms obstructive coronary lesions diagnosed by CCTA, only a minority lead to coronary ischemia. Therefore, for moderate coronary stenosis determined by CCTA, a functional test is now recommended prior to ICA referral [16].

The need for a validated noninvasive diagnostic method is clear, and the CT-FFR, based on CFD, presents a promising solution. It can accurately identify the hemodynamic effects of lesions and has the potential to significantly reduce unnecessary ICA. A prospective multicenter trial demonstrated the feasibility of CT-FFR, showing a reduction of up to 61% of potential ICA procedures [17]. Furthermore, stable CAD patients with a negative CT-FFR ($>$0.80) experienced low cardiovascular adverse events at a 12-month follow-up [18]. The advantages of the CT-FFR extend beyond accuracy and include software that aligns with existing CCTA datasets. There is no need to change the data collection protocol, provide additional images, or administer drugs, further streamlining the process.

We investigated novel prototype software for deriving the CT-FFR from CCTA data, which we then compared with the FFR. Previous CT-FFR studies have used a 0.80 threshold to detect lesion-specific ischemia in comparisons with the FFR [19, 20, 21, 22, 23]. Our study revealed that the CT-FFR threshold of 0.80 provided good diagnostic accuracy, sensitivity, and specificity (97.1%, 96.2%, and 97.7% respectively), with an AUC of 0.98, exceeding results from previous studies (Table 6, Ref. [19, 20, 21, 23, 24, 25, 26, 27, 28]). This advancement may be attributed to improvements in the CT-FFR algorithm, the incorporation of deep learning analysis, and unique boundary conditions applied to the new software research prototype. Furthermore, the CT-FFR threshold of 0.75 also exhibited solid diagnostic accuracy, sensitivity, and specificity (84.1%, 78.8%, and 85.7% respectively), with an AUC of 0.95. A direct correlation between CT-FFR and FFR was established (p 0.001) without systematic differences in this study. Cumulatively, these results underscore the CT-FFR’s high diagnostic accuracy in identifying coronary ischemia.

While the study yielded promising insights, several limitations and unaddressed areas must be acknowledged. First, the relatively low number of samples could influence the robustness of the findings. The inclusion criteria, including 30%–90% coronary artery stenosis, may have introduced selection bias, potentially skewing the results. Furthermore, specific patient conditions, such as previous coronary artery bypass surgery (CABG) and stent implantation, were excluded from the study. This leaves the diagnostic value of the CT-FFR of such patients an open question that requires further examination. Finally, we did not report any clinical outcome observations for CT-FFR–guided revascularization, leaving an area for future exploration.

In this prospective trial, we utilized novel CT-FFR software to analyze CCTA data, comparing its findings with the established FFR. The key results include a strong direct correlation between the CT-FFR and FFR, along with high diagnostic performance for lesion-specific ischemia, particularly within a stenosis rate of 30%–90%. This study highlights the accuracy and clinical value of the CT-FFR, particularly when leveraging deep learning analysis. However, the findings are subject to certain limitations, notably the specificity of the inclusion and exclusion criteria, making them applicable only to specific patients and types of coronary stenosis. Consequently, the general applicability of the current conclusions require further study. Additionally, future studies should evaluate the novel CT-FFR software’s impact on CAD patients’ prognosis and compare it with other CT-FFR software solutions.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

HW, WH, ZY, JQ, JD and YT contributed in the data collection, statistical analysis and manuscript drafting. HW, LL, XS and HC participated in data collection and manuscript revision. HW, LL, FQ, XS and HC was responsible for the study design, manuscript revision and consultation. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Shaanxi Provincial People’s Hospital (Approval No. 2019-X005). All subjects gave their informed consent for inclusion before they participated in the study.

Not applicable.

This research was partially supported by the Research and Development Program of Shaanxi Province (No. 2022ZDLSF02-03) and the Science and Technology Talent Support Program of Shaanxi Provincial People’s Hospital (No. 2021LJ-09, 2021JY-24).

The authors declare no conflict of interest.