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Peer-reviewedCC BY-NC-ND 4.0JACC Case Reports

Reducing Diagnostic Variation With Hybrid AI Coronary CTA Interpretation

  • Barilee Abueh1
  • Michael Beshara1
  • Qwynton Johnson1
  • Wing Lam Ho1
  • Ryung San Lee1
  • Brian John Page2
  • Umesh Sharma2
  1. 1Department of Internal Medicine, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
  2. 2Division of Cardiovascular Medicine, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA
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Abstract

Background: In hospitalized patients undergoing coronary computed tomography angiography (CCTA), variability in stenosis interpretation may contribute to missed obstructive coronary artery disease (CAD) or unnecessary invasive coronary angiography.

Project Summary: We retrospectively evaluated inpatient CCTA interpretation workflows at a tertiary academic center between 2023 and 2025. Among 177 hospitalized patients undergoing CCTA, 139 had both artificial intelligence (AI)–assisted and physician interpretations available for comparison. Twenty patients underwent same-admission invasive coronary angiography, with 19 included in final invasive angiographic analysis. AI-assisted interpretation demonstrated higher agreement with invasive coronary angiography than physician interpretation within the exploratory invasive angiography subgroup (78.6% vs 55.0%). Discordance patterns were primarily driven by false-negative physician interpretations and AI-assisted stenosis overestimation. Significant CAD was identified in 73.7% of the invasive angiography subgroup, with 52.6% ultimately undergoing revascularization.

Take-Home Message: Hybrid AI-physician interpretation workflows may support more consistent inpatient CAD evaluation while preserving physician-guided clinical decision making.

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Cite

BibTeXRIS
BibTeX
@article{abueh2026reducing,
  title = {{Reducing Diagnostic Variation With Hybrid AI Coronary CTA Interpretation}},
  author = {Barilee Abueh and Michael Beshara and Qwynton Johnson and Wing Lam Ho and Ryung San Lee and Brian John Page and Umesh Sharma},
  journal = {JACC Case Reports},
  year = {2026},
  month = sep,
  doi = {10.1016/j.jaccas.2026.109115},
  url = {https://doi.org/10.1016/j.jaccas.2026.109115},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Reducing Diagnostic Variation With Hybrid AI Coronary CTA Interpretation
AU  - Barilee Abueh
AU  - Michael Beshara
AU  - Qwynton Johnson
AU  - Wing Lam Ho
AU  - Ryung San Lee
AU  - Brian John Page
AU  - Umesh Sharma
PY  - 2026
DA  - 2026/09/08
JO  - JACC Case Reports
DO  - 10.1016/j.jaccas.2026.109115
UR  - https://doi.org/10.1016/j.jaccas.2026.109115
AB  - Background: In hospitalized patients undergoing coronary computed tomography angiography (CCTA), variability in stenosis interpretation may contribute to missed obstructive coronary artery disease (CAD) or unnecessary invasive coronary angiography.  Project Summary: We retrospectively evaluated inpatient CCTA interpretation workflows at a tertiary academic center between 2023 and 2025. Among 177 hospitalized patients undergoing CCTA, 139 had both artificial intelligence (AI)–assisted and physician interpretations available for comparison. Twenty patients underwent same-admission invasive coronary angiography, with 19 included in final invasive angiographic analysis. AI-assisted interpretation demonstrated higher agreement with invasive coronary angiography than physician interpretation within the exploratory invasive angiography subgroup (78.6% vs 55.0%). Discordance patterns were primarily driven by false-negative physician interpretations and AI-assisted stenosis overestimation. Significant CAD was identified in 73.7% of the invasive angiography subgroup, with 52.6% ultimately undergoing revascularization.  Take-Home Message: Hybrid AI-physician interpretation workflows may support more consistent inpatient CAD evaluation while preserving physician-guided clinical decision making.
SN  - 2666-0849
ER  -