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Updated: Jul 11, 2025
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
Published on: April 11, 2025
Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular
Johanna E J Jacobs1,2, Grace Greason1, Kathryn E Mangold1
1Department of Cardiovascular Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA.
Aims:
Cardiotoxicity is a serious side effect of anthracycline treatment, most commonly manifesting as a reduction in left ventricular ejection fraction (EF). Early recognition and treatment have been advocated, but robust, convenient, and cost-effective alternatives to cardiac imaging are missing. Recent developments in artificial intelligence (AI) techniques applied to electrocardiograms (ECGs) may fill this gap, but no study so far has demonstrated its merit for the detection of an abnormal EF after anthracycline therapy.
Methods And Results:
Single centre consecutive cohort study of all breast cancer patients with ECG and transthoracic echocardiography (TTE) evaluation before and after (neo)adjuvant anthracycline chemotherapy. Patients with HER2-directed therapy, metastatic disease, second primary malignancy, or pre-existing cardiovascular disease were excluded from the analyses as were patients with EF decline for reasons other than anthracycline-induced cardiotoxicity. Primary readout was the diagnostic performance of AI-ECG by area under the curve (AUC) for EFs < 50%. Of 989 consecutive female breast cancer patients, 22 developed a decline in EF attributed to anthracycline therapy over a follow-up time of 9.8 ± 4.2 years. After exclusion of patients who did not have ECGs within 90 days of a TTE, 20 cases and 683 controls remained. The AI-ECG model detected an EF < 50% and ≤ 35% after anthracycline therapy with an AUC of 0.93 and 0.94, respectively.
Conclusion:
These data support the use of AI-ECG for cardiotoxicity screening after anthracycline-based chemotherapy. This technology could serve as a gatekeeper to more costly cardiac imaging and could enable patients to monitor themselves over long periods of time.
Insights
Artificial intelligence applied to electrocardiograms (AI-ECG) shows promise for detecting reduced left ventricular ejection fraction (EF) in breast cancer patients undergoing anthracycline chemotherapy. This AI-ECG tool could facilitate early cardiotoxicity screening.
Area of Science:
- Cardiology
- Oncology
- Artificial Intelligence
Background:
- Anthracycline chemotherapy can cause cardiotoxicity, often indicated by reduced left ventricular ejection fraction (EF).
- Current methods for detecting cardiotoxicity, such as cardiac imaging, are costly and not always convenient.
- There is a need for accessible and cost-effective screening tools for anthracycline-induced cardiotoxicity.
Purpose of the Study:
- To evaluate the diagnostic performance of an artificial intelligence-electrocardiogram (AI-ECG) model in detecting reduced EF after anthracycline therapy.
- To assess AI-ECG as a potential screening tool for cardiotoxicity in breast cancer patients.
Main Methods:
- A single-center cohort study included breast cancer patients receiving anthracycline chemotherapy.
- Electrocardiograms (ECGs) and transthoracic echocardiography (TTE) were performed before and after chemotherapy.
- An AI-ECG model was used to detect reduced EF (<50% and ≤35%), with diagnostic performance measured by area under the curve (AUC).
Main Results:
- The study analyzed 989 patients, with 22 developing anthracycline-attributed EF decline.
- After exclusions, 20 cases and 683 controls were analyzed.
- The AI-ECG model achieved an AUC of 0.93 for detecting EF <50% and 0.94 for EF ≤35%.
Conclusions:
- AI-ECG demonstrates high accuracy in detecting reduced EF following anthracycline chemotherapy.
- This technology shows potential as a cost-effective screening tool for cardiotoxicity.
- AI-ECG could serve as a gatekeeper for more expensive cardiac imaging and enable long-term patient monitoring.
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