Key result
Machine learning ECG algorithms improve LVH sensitivity but fail to outperform classic criteria specificity.
Why the study?
Over a dozen different ML algorithms exist for detecting LVH on 12-lead ECG, motivating an evaluation of their performance compared with classic ECG criteria.
Do machine learning algorithms improve the detection of left ventricular hypertrophy from a 12-lead ECG compared to classic ECG criteria?
Do machine learning algorithms improve the detection of left ventricular hypertrophy from a 12-lead ECG compared to classic ECG criteria?
Machine learning algorithms improve the sensitivity of ECG-based LVH detection but do not consistently outperform classic criteria in specificity, highlighting the need for standardized testing databases.
ML may boost ECG sensitivity for LVH detection; leaves open whether standardized databases can establish specificity gains over classic criteria.
: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.
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Simon W. Rabkin (2024) studied this question. Machine learning algorithms for ECG-based detection of left ventricular hypertrophy improve sensitivity but generally fail to outperform the specificity of classic ECG criteria.
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