Key result
Wearable single-lead ECG AI detects post-thyroidectomy hypocalcemia with ~0.88 AUC, outperforming QTc.
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Why the study?
Timely recognition of post-thyroidectomy hypocalcemia remains challenging, and improved early detection methods are needed.
Does a deep learning model using wearable single-lead electrocardiograms improve early detection of post-thyroidectomy hypocalcemia compared to the standard corrected QT interval benchmark?
Cohort (n=51)
No
Does a deep learning model using wearable single-lead electrocardiograms improve early detection of post-thyroidectomy hypocalcemia compared to the standard corrected QT interval benchmark?
Effect estimate: AUC 0.878
Absolute Event Rate: 0.878% vs 0.526%
A deep learning algorithm applied to wearable single-lead ECGs accurately detected post-thyroidectomy hypocalcemia, significantly outperforming standard QTc interval measurements.
BACKGROUND Timely recognition of post-thyroidectomy hypocalcemia remains challenging. We evaluated whether deep learning models using wearable single-lead electrocardiograms detect biochemical early warning hypocalcemia, defined as albumin-corrected calcium <8.5 mg/dL, compared with a heart rate-corrected QT interval benchmark. METHODS In a prospective cohort (N = 51), at baseline and postoperative day 1, postoperative day 2, 2-3 weeks, and 2-3 months, we obtained synchronized laboratory measurements and four 30-second wearable single-lead electrocardiogram recordings within 60 minutes and recorded symptom severity. Data were split at the patient level (no overlap) into internal and external cohorts for training, validation, and testing. We evaluated DenseNet, EfficientNet, ResNet, ResNeXt, and RegNet architectures. The primary end point was the area under the receiver operating characteristic curve for corrected calcium <8.5 mg/dL. Corrected QT interval from contemporaneous 12-lead electrocardiograms served as the non-deep learning benchmark. RESULTS We analyzed 490 electrocardiogram images from 38 patients (internal: 31; external: 7). DenseNet achieved area under the receiver operating characteristic curves of 0.878 (internal) and 0.870 (external) for corrected calcium <8.5 mg/dL, whereas postoperative day 1 12-lead corrected QT interval showed limited discrimination (area under the receiver operating characteristic curve, 0.526). In the full cohort (N = 51), the prevalence of biochemical early warning hypocalcemia was 60.8% (postoperative day 1), 58.8% (postoperative day 2), 17.6% (2-3 weeks), and 29.4% (2-3 months); the prevalence of biochemically significant hypocalcemia with low parathyroid hormone was 27.5%, 27.5%, 2.0%, and 0%, respectively. CONCLUSION A wearable single-lead electrocardiogram-based deep learning model demonstrated strong discriminative performance for biochemical hypocalcemia after thyroidectomy. Compared with corrected QT interval-based assessment, the model achieved superior performance. Larger multi-institutional validation is warranted before clinical deployment.
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Lee et al. (2026) conducted a cohort in post-thyroidectomy hypocalcemia (n=51). Deep learning models using wearable single-lead electrocardiograms vs. Heart rate-corrected QT interval from contemporaneous 12-lead electrocardiograms was evaluated on area under the receiver operating characteristic curve for corrected calcium <8.5 mg/dL (AUC 0.878). A deep learning model using wearable single-lead electrocardiograms achieved superior discrimination for post-thyroidectomy hypocalcemia compared with the corrected QT interval (AUC 0.878 vs 0.526).
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