{"id":"AIBD-1163","title":"Singapore's ACE finds that the Anumana and Eko AI-ECG tools detect a weak heart pump safely and with moderate accuracy, with limited proof of benefit","summary":"Singapore's ACE found that the Anumana and Eko AI-ECG tools were safe and moderately accurate in detecting an ejection fraction of 40% or less. Their negative predictive value was 98% to 100%.","date_observed":"2026-04-30","date_basis":"source publication","date_precision":"day","benefit_status":"mixed","source_collection":null,"source_entry":null,"account":"Anumana's ECG-AI LEF and Eko's ELEFT, both retrained from a Mayo Clinic algorithm, flag a left ventricular ejection fraction of 40% or less from an electrocardiogram. Singapore's Agency for Care Effectiveness (ACE) reviewed the evidence in a brief published on 30 April 2026, and it writes that AI-ECG uses deep learning. ACE drew on two randomized trials, four prospective observational studies and one cost-effectiveness study. Against echocardiography, sensitivity ranged from 76.5% to 100% and specificity from 78.3% to 97%. Negative predictive value was 98% to 100%, and positive predictive value was 16.4% to 43.3%. No serious adverse events were reported. In the EAGLE trial, low ejection fraction was found in 0.9% of patients whose clinicians saw the AI results, against 0.6% of controls (p=0.057). In a Nigerian trial, cardiovascular events did not differ between arms. All-cause deaths were higher in its AI arm, from non-cardiovascular causes. ACE found that evidence for clinical utility remains limited. Asian patients made up 0% to 19.9% of study populations. ACE notes that the brief rests on a limited literature search and does not appraise the studies.","category":"aggregate result","unexpected":false,"purpose":"Anumana and Eko Health built ECG-AI LEF and ELEFT to help clinicians identify adults at risk of heart failure whose left ventricular ejection fraction is 40% or less.","system":"Anumana ECG-AI LEF 12-Lead algorithm and Eko Low Ejection Fraction Tool (ELEFT)","model_version":null,"developer":"Anumana, Inc. and Eko Health, Inc., both building on Mayo Clinic's 12-lead AI-ECG algorithm","deployer":"Clinicians and sites in the seven studies that ACE reviewed, including the EAGLE trial's primary care teams and the SPEC-AI Nigeria trial (not in routine use in Singapore)","location":"Study sites including Nigeria, assessed for Singapore","domain":"Healthcare","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.1.b.1","4.A.2.a.4"],"taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"personal","depth":null,"reason":"Accuracy was measured against echocardiography, and in the EAGLE trial neither detection nor treatment changed to a statistically significant degree."}],"ai_role":{"status":"assessed","statement":"The EAGLE trial compared primary care teams with and without access to the AI results, and the SPEC-AI Nigeria trial compared AI-guided screening with usual care. In neither trial was a gain in detection or outcomes statistically significant. ACE writes that AI-ECG uses deep learning and that both tools retrain the Mayo Clinic algorithm."},"evidence":[{"kind":"assessment report","citation":"Agency for Care Effectiveness (ACE), Ministry of Health, Singapore. Artificial intelligence-enabled electrocardiogram (AI-ECG) to detect reduced left ventricular ejection fraction. ACE Brief for New and Emerging Health Technologies, HSB-M 01/2026, dated December 2025, published on 30 April 2026. Summary of Key Points, p. 1.","title":"Artificial intelligence-enabled electrocardiogram (AI-ECG) to detect reduced left ventricular ejection fraction (HSB-M 01/2026)","url":"https://isomer-user-content.by.gov.sg/68/628aff49-ab5c-4ffb-93f2-ff76f4721da6/Artificial%20intelligence-enabled%20electrocardiogram%20(AI-ECG)%20to%20detect%20reduced%20left%20ventricular%20ej.pdf","date":"2026-04-30","written_by":"independent","quote":"Overall, Eko and Anumana AI-ECG tools were found to be safe with moderate accuracy in detecting LVEF ≤40%."},{"kind":"assessment report","citation":"Agency for Care Effectiveness (ACE), Ministry of Health, Singapore. Artificial intelligence-enabled electrocardiogram (AI-ECG) to detect reduced left ventricular ejection fraction. ACE Brief for New and Emerging Health Technologies, HSB-M 01/2026, dated December 2025, published on 30 April 2026. Section VI, Effectiveness, Clinical Utility, p. 8.","title":"Artificial intelligence-enabled electrocardiogram (AI-ECG) to detect reduced left ventricular ejection fraction (HSB-M 01/2026)","url":"https://isomer-user-content.by.gov.sg/68/628aff49-ab5c-4ffb-93f2-ff76f4721da6/Artificial%20intelligence-enabled%20electrocardiogram%20(AI-ECG)%20to%20detect%20reduced%20left%20ventricular%20ej.pdf","date":"2026-04-30","written_by":"independent","quote":"Findings from the EAGLE RCT showed that compared to usual care, the use of Anumana ECG-AI LEF did not result in significant changes in the detection of low LVEF (≤40%) or clinical management.","supports_benefit":false}],"replication":{"produced_by":["Anumana's algorithm analyzed standard 12-lead ECGs, and Eko's ELEFT analyzed single-lead ECG and heart sounds from the Eko DUO digital stethoscope.","Each tool returned a binary result for an ejection fraction of 40% or less."],"must_hold":["Patients are adults at risk of heart failure.","Clinicians use the result with their own evaluation and clinical judgment, and an echocardiogram still confirms heart failure."],"echoes":[]},"paired_incident":null,"headline":"*ECG-AI LEF* and *ELEFT* flag weak heart pumps with moderate accuracy","metric":"negative predictive value was 98% to 100%","comparator":{"against":"Primary care teams without access to the AI-ECG results in the EAGLE trial","before":"70/11,068 (0.6%)","after":"102/11,573 (0.9%)","basis":"measured","quote":"While a numerically higher proportion of patients managed by clinicians with access to the AI-ECG results had lower LVEF detected than those in the control group, this difference did not reach statistical significance (0.9% vs. 0.6%, p=0.057; Table 5).","url":"https://isomer-user-content.by.gov.sg/68/628aff49-ab5c-4ffb-93f2-ff76f4721da6/Artificial%20intelligence-enabled%20electrocardiogram%20(AI-ECG)%20to%20detect%20reduced%20left%20ventricular%20ej.pdf"},"later":null,"value":null,"developer_country":"United States","deployer_country":"Multiple","unrated_because":"In the trials, gains in detection and outcomes were not statistically significant, so no realized benefit is reported for any group.","brief_notes":null,"brief":null,"revisions":[{"date":"2026-11-01","change":"Added the record from the finding of Singapore's Agency for Care Effectiveness (ACE).","source":"https://isomer-user-content.by.gov.sg/68/628aff49-ab5c-4ffb-93f2-ff76f4721da6/Artificial%20intelligence-enabled%20electrocardiogram%20(AI-ECG)%20to%20detect%20reduced%20left%20ventricular%20ej.pdf","editor":"AIBD"}],"confidence":"found","shade":"high","claim_status":"Found by an assessment body","claim_basis":"Sources: one independent assessment report. 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ACE Brief for New and Emerging Health Technologies, HSB-M 01/2026, dated December 2025, published on 30 April 2026. Section VI, Effectiveness, Clinical Utility, p. 8.","title":"Artificial intelligence-enabled electrocardiogram (AI-ECG) to detect reduced left ventricular ejection fraction (HSB-M 01/2026)","url":"https://isomer-user-content.by.gov.sg/68/628aff49-ab5c-4ffb-93f2-ff76f4721da6/Artificial%20intelligence-enabled%20electrocardiogram%20(AI-ECG)%20to%20detect%20reduced%20left%20ventricular%20ej.pdf","date":"2026-04-30","written_by":"independent","quote":"Findings from the EAGLE RCT showed that compared to usual care, the use of Anumana ECG-AI LEF did not result in significant changes in the detection of low LVEF (≤40%) or clinical management.","supports_benefit":false}]}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["evidence"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"sources[].id":{"state":"value_present","value":{"kind":"identifiers","identifiers":["AIBD-1163-source-0001","AIBD-1163-source-0002"],"role":"Existing evidence items, in their original order"},"provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"sources[].language":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"sources[].relationship":{"state":"value_present","value":{"kind":"record_fields","fields":{"evidence":[{"kind":"assessment report","citation":"Agency for Care Effectiveness (ACE), Ministry of Health, Singapore. 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