TY - JOUR
T1 - Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment
AU - Seligman, Henry
AU - Patel, Sapna B
AU - Alloula, Anissa
AU - Howard, James P
AU - Cook, Christopher M
AU - Ahmad, Yousif
AU - de Waard, Guus A
AU - Pinto, Mauro Echavarría
AU - van de Hoef, Tim P
AU - Rahman, Haseeb
AU - Kelshiker, Mihir A
AU - Rajkumar, Christopher A
AU - Foley, Michael
AU - Nowbar, Alexandra N
AU - Mehta, Samay
AU - Toulemonde, Mathieu
AU - Tang, Meng-Xing
AU - Al-Lamee, Rasha
AU - Sen, Sayan
AU - Cole, Graham
AU - Nijjer, Sukhjinder
AU - Escaned, Javier
AU - Van Royen, Niels
AU - Francis, Darrel P
AU - Shun-Shin, Matthew J
AU - Petraco, Ricardo
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2023/8
Y1 - 2023/8
N2 - Aims: Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity. Methods and results: A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, P < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, P < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console). Conclusion: An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.
AB - Aims: Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity. Methods and results: A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, P < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, P < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console). Conclusion: An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.
U2 - 10.1093/ehjdh/ztad030
DO - 10.1093/ehjdh/ztad030
M3 - Article
C2 - 37538145
SN - 2634-3916
VL - 4
SP - 291
EP - 301
JO - European Heart Journal - Digital Health
JF - European Heart Journal - Digital Health
IS - 4
ER -