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. 2023 Jun 29;29(7):1804–1813. doi: 10.1038/s41591-023-02396-3

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 5 — This figure provides a selected example of a patient with occlusion myocardial infarction that was missed by the machine learning model and other reference standards. This ECG was obtained on a 70-year-old female with a past medical history of hypertension, high cholesterol, prior myocardial infarction, and current smoking. The baseline clinical interpretation suggests normal sinus rhythm with benign findings. There are isolated Q waves in inferior leads, low ECG voltage, and some baseline wander and high frequency noise in few leads. The OMI risk score was 2 indicating a low risk. The patient was later sent to the catheterization lab, which showed severe left main occlusion and had many stents placed. The patient developed new-onset HF during hospitalization. A closer look at this ECG by experienced ECG readers suggests that this ECG could resemble the ‘precordial swirl pattern’, a rightward ST-elevation vector, with STE in V1 and aVR and reciprocal ST-depression in V5 and V6. This pattern was found to correlate with LAD occlusion.