The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Diagnostic Performance of Computer Programs for the Interpretation of Electrocardiograms
Open accessAbstract
BACKGROUND: Computer programs for the interpretation of electrocardiograms (ECGs) are now widely used. However, a systematic assessment of various computer programs for the interpretation of ECGs has not been performed. METHODS: We undertook a large international study to compare the performance of nine electrocardiographic computer programs with that of eight cardiologists in interpreting ECGs in 1220 clinically validated cases of various cardiac disorders. ECGs from the following groups were included in the sample: control patients (n = 382); patients with left ventricular hypertrophy (n = 183), right ventricular hypertrophy (n = 55), or biventricular hypertrophy (n = 53); patients with anterior myocardial infarction (n = 170), inferior myocardial infarction (n = 273), or combined myocardial infarction (n = 73); and patients with combined infarction and hypertrophy (n = 31). The interpretations of the computer programs and the cardiologists were compared with the clinical diagnoses made independently of the ECGs, and the computer interpretations were compared with those of the cardiologists. RESULTS: The percentage of ECGs correctly classified by the computer programs (median, 91.3 percent) was lower than that of the cardiologists (median, 96.0 percent; P less than 0.01). The median sensitivity of the computer programs was also significantly lower than that of the cardiologists in diagnosing left ventricular hypertrophy (56.6 percent vs. 63.9 percent, P less than 0.02), right ventricular hypertrophy (31.8 percent vs. 46.6 percent, P less than 0.01), anterior myocardial infarction (77.1 percent vs. 84.9 percent, P less than 0.001), and inferior myocardial infarction (58.8 percent vs. 71.7 percent, P less than 0.0001). The median total accuracy level (the percentage of correct classifications) was 6.6 percent lower for the computer programs (69.7 percent) than for the cardiologists (76.3 percent; P less than 0.001). However, the performance of the best programs nearly matched that of the most accurate cardiologists. CONCLUSIONS: Our study shows that some but not all computer programs for the interpretation of ECGs perform almost as well as cardiologists in identifying seven major cardiac disorders.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Cited by
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study
Heeringa, J., van der Kuip, D. A. M., Hofman, A., Kors, J. A., van Herpen, G., Stricker, B. H. C., Stijnen, T., Lip, G. Y. H., & Witteman, J. C. M. (2006) · European Heart Journal · doi:10.1093/eurheartj/ehi825 · Cited by 1,939
Hak, A. E., Pols, H. A. P., Visser, T. J., Drexhage, H. A., Hofman, A., & Witteman, J. C. M. (2000) · Annals of Internal Medicine · doi:10.7326/0003-4819-132-4-200002150-00004 · Cited by 1,336
Automatic diagnosis of the 12-lead ECG using a deep neural network
Ribeiro, A. H., Ribeiro, M. H., Paixão, G. M. M., Oliveira, D. M., Gomes, P. R., Canazart, J. A., Ferreira, M. P. S., Andersson, C. R., Macfarlane, P. W., Meira, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15432-4 · Cited by 894
Self-citationProlonged QTc Interval and Risk of Sudden Cardiac Death in a Population of Older Adults
Straus, S. M. J. M., Kors, J. A., De Bruin, M. L., van der Hooft, C. S., Hofman, A., Heeringa, J., Deckers, J. W., Kingma, J. H., Sturkenboom, M. C. J. M., Stricker, B. H. C., et al. (2006) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2005.08.067 · Cited by 833
Metadata
- Authors
- Willems, J. L., Abreu-Lima, C., Arnaud, P., van Bemmel, J. H., Brohet, C., Degani, R., Denis, B., Gehring, J., Graham, I., van Herpen, G., Machado, H., Macfarlane, P. W., Michaelis, J., Moulopoulos, S. D., Rubel, P., & Zywietz, C.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 1991-12-19
- Volume
- 325
- Issue
- 25
- Pages
- 1767-1773
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2017086716
- Cited by
- 539
- References
- 1
- FWCI
- 9.52
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Diagnostic Performance of Computer Programs for the Interpretation of Electrocardiograms. https://app.cochat.ai/papers/10.1056/nejm199112193252503
- The paper
- Willems, J. L., Abreu-Lima, C., Arnaud, P., van Bemmel, J. H., Brohet, C., Degani, R., Denis, B., Gehring, J., Graham, I., van Herpen, G., Machado, H., Macfarlane, P. W., Michaelis, J., Moulopoulos, S. D., Rubel, P., & Zywietz, C. (1991). The Diagnostic Performance of Computer Programs for the Interpretation of Electrocardiograms. New England Journal of Medicine. https://doi.org/10.1056/nejm199112193252503
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.