Early Feasibility of Automated Artificial Intelligence Angiography Based Fractional Flow Reserve Estimation

Ariel Roguin, Ala Abu Dogosh, Yair Feld, Maayan Konigstein, Amir Lerman, Edward Koifman*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Despite the evidence of improved patients' outcome, fractional flow reserve (FFR) is underused in current everyday practice. We aimed to evaluate the feasibility of a novel automated artificial intelligence angiography-based FFR software (AutocathFFR) as a decision supporting tool for interventional cardiologists. AutocathFFR was performed on angiographic images of patients who underwent coronary angiography with a pressure wire FFR measurement. Sensitivity and specificity for detection of FFR cut-off of 0.8 were calculated. Thirty-one patients were included in the present study, with a mean age of 64 ± 10 years, 80% were males, 32% patients had diabetes, 39% had previous percutaneous coronary intervention. The left anterior descending artery was the target vessel in 80% of patients. Automatic lesion detection was successful in all of the lesions with FFR value of ≤0.8. The sensitivity of AutocathFFR for predicting a wire based FFR ≤0.8 was 88% and the specificity for FFR >0.8 was 93%, with a positive predictive value of 94% and negative predictive value of 87%, indicating an accuracy level of 90% and area under the curve of 0.91. AutocathFFR has excellent accuracy in prediction of wire based FFR and is a promising technology that may facilitate appropriate decision and treatment choices for coronary artery disease patients.

Original languageEnglish
Pages (from-to)8-14
Number of pages7
JournalAmerican Journal of Cardiology
StatePublished - 15 Jan 2021
Externally publishedYes


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