AI Article Synopsis
- The study aims to improve the clinical use of coronary flow reserve (CFR) assessment by developing AI algorithms that automatically evaluate coronary Doppler signal quality and measure flow velocity.
- A neural network was trained on Doppler flow recordings, achieving high accuracy in quantifying signal quality and flow measurements, outperforming traditional console methods and aligning closely with expert assessments.
- The conclusion suggests that an AI-driven system can enhance the precision and reliability of CFR measurements, making it more feasible for clinical settings and reducing dependence on operator skills.
Article Abstract
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, < 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, < 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393887 | PMC |
| http://dx.doi.org/10.1093/ehjdh/ztad030 | DOI Listing |
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