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. 2024 Mar 26;3(3Part B):101296.
doi: 10.1016/j.jscai.2024.101296. eCollection 2024 Mar.

Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study

Sarah Rinehart  1 , Steven J Raible  2 , Nicholas Ng  3 , Sarah Mullen  3 , Whitney Huey  3 , Campbell Rogers  3 , Amit Pursnani  4
Affiliations

Utility of Artificial Intelligence Plaque Quantification: Results of the DECODE Study

Sarah Rinehart et al. J Soc Cardiovasc Angiogr Interv. .

Erratum in

  • Correction.
    [No authors listed] [No authors listed] J Soc Cardiovasc Angiogr Interv. 2025 Aug 9;4(10Part A):103930. doi: 10.1016/j.jscai.2025.103930. eCollection 2025 Oct. J Soc Cardiovasc Angiogr Interv. 2025. PMID: 41268087 Free PMC article.

Abstract

Background: Artificial Intelligence Plaque Analysis (AI-QCPA, HeartFlow) provides, from a CCTA, quantitative plaque burden information including total plaque and plaque subtype volumes. We sought to evaluate the clinical utility of AI-QCPA in clinical decision making.

Methods: One hundred cases were reviewed by 3 highly experienced practicing cardiologists who are SCCT level 3 CCTA readers. Patients had varying levels of calcium (median CACS: 99.5) and CAD-RADS scores. Initial management plan for each case was a majority decision based upon patient demographics, clinical history, and CCTA report. AI-QCPA was then provided for each patient, and the plan was reconsidered. The primary endpoint was the reclassification rate (RR). In a secondary analysis of 40 cases, the above process was repeated but the initial plan was based upon review of the actual CCTA images.

Results: RR following AI-QCPA review was 66% (66/100) of cases (95% CI, 56.72%-75.28%). RR ranged from 47% in cases with CACS 0 to 96% in cases with CACS >400, and from 40% in CAD-RADS 1 cases to 94% in CAD-RADS 4 cases. RR was higher in cases with coronary stenoses ≥50% (89.5%) vs cases with stenoses <50% (51.6%). RR was 39% in cases with LDL <70 mg/dL vs 70% in LDL ≥70 mg/dL. Following review of the CCTA images rather than the CCTA report, the RR was 50% (95% CI of 34.51% - 65.49%). The primary reclassification effect was to intensify preventative medical therapy.

Conclusions: Adding AI-QCPA to CCTA alone leads to a change in clinical care in two-thirds of patients.

Keywords: artificial intelligence quantified coronary plaque analysis; atherosclerosis; coronary artery disease; coronary computed tomographic angiography; coronary plaque; coronary plaque quantification.

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Figures

Figure 1
Figure 1
Study design. AI-QCPA, artificial intelligence quantified coronary plaque analysis; CAD, coronary artery disease; CCTA, coronary computed tomographic angiography.
Figure 2
Figure 2
Medical management staging.
Figure 3
Figure 3
Patient example. AI-QCPA, artificial intelligence quantified coronary plaque analysis; CCTA, coronary computed tomographic angiography.
Central Illustration
Central Illustration
Reclassification rate following artificial intelligence quantified coronary plaque analysis. CCTA, coronary computed tomographic angiography.
See this image and copyright information in PMC

References

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