Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation

Hughes, J. Weston; Olgin, Jeffrey E.; Avram, Robert; Abreau, Sean; Sittler, Taylor; Radia, Kaahan; Hsia, Henry H.; Walters, Tomos E.; Lee, Byron; Gonzalez, Joseph E.; Tison, Geoffrey H.

doi:10.1001/jamacardio.2021.2746

Cited by 91 publications

(49 citation statements)

References 29 publications

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“…We used Acc (Accuracy), Precision, Recall, and F1 score to evaluate the experimental results. We used a long short-term memory [ 17 , 18 ] sparse auto encoder (LSAE) to reduce the dimension and the interactive fusion method to compare the prediction effect of different models. It should be noted that we also compared multitasking separately.…”

Section: Methodsmentioning

confidence: 99%

Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning

Zuo

Zhang

et al. 2022

Journal of Healthcare Engineering

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Objective. Diabetic complications have brought a tremendous burden for diabetic patients, but the problem of predicting diabetic complications is still unresolved. Our aim is to explore the relationship between hemoglobin A1C (HbA1c), insulin (INS), and glucose (GLU) and diabetic complications in combination with individual factors and to effectively predict multiple complications of diabetes. Methods. This was a real-world study. Data were collected from 40,913 participants with an average age of 48 years from the Department of Endocrinology of Ruijin Hospital in Shanghai. We proposed deep personal multitask prediction of diabetes complication with attentive interactions (DPMP-DC) to predict the five complication models of diabetes, including diabetic retinopathy, diabetic nephropathy, diabetic peripheral neuropathy, diabetic foot disease, and diabetic cardiovascular disease. Results. Our model has an accuracy rate of 88.01% for diabetic retinopathy, 89.58% for diabetic nephropathy, 85.77% for diabetic neuropathy, 80.56% for diabetic foot disease, and 82.48% for diabetic cardiovascular disease. The multitasking accuracy of multiple complications is 84.67%, and the missed diagnosis rate is 9.07%. Conclusion. We put forward the method of interactive integration with individual factors of patients for the first time in diabetic complications, which reflect the differences between individuals. Our multitask model using the hard sharing mechanism provides better prediction than prior single prediction models.

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Section: Methodsmentioning

confidence: 99%

Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning

Zuo

Zhang

et al. 2022

Journal of Healthcare Engineering

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“…Early stopping was performed based on the validation dataset’s area under the receiver operating curve. Local Interpretable Model-agnostic Explanations (LIME)(15, 19) was used with 1000 samples per study to identify relevant features in the ECG waveform by iteratively randomly perturbing 0.5% of the waveform and identifying which changes most impacted model performance.…”

Section: Methodsmentioning

confidence: 99%

Deep learning based electrocardiographic screening for chronic kidney disease

Holmström

Christensen

Yuan

et al. 2022

Preprint

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BackgroundUndiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs.MethodsWe collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs from between 2005 and 2018.ResultsUsing 12-lead ECG waveforms, our deep learning algorithm achieved discrimination for CKD of any stage with an AUC of 0.77 (95% CI 0.76-0.77) in a held-out test set and an AUC of 0.71 (0.71-0.71) in the external cohort. Our 12-lead ECG-based model performance was consistent across the severity of CKD, with an AUC of 0.75 (0.0.74-0.77) for mild CKD, AUC of 0.76 (0.75-0.77) for moderate-severe CKD, and an AUC of 0.78 (0.77-0.79) for ESRD. In our internal health system with 1-lead ECG waveform data, our model achieved an AUC of 0.74 (0.74-0.75) in detecting any stage CKD. In the external cohort, our 1-lead ECG-based model achieved an AUC of 0.70 (0.70-0.70). In patients under 60 years old, our model achieved high performance in detecting any stage CKD with both 12-lead (AUC 0.84 [0.84-0.85]) and 1-lead ECG waveform (0.82 [0.81-0.83]).ConclusionsOur deep learning algorithm was able to detect CKD using ECG waveforms, with particularly strong performance in younger patients and patients with more severe stages of CKD. Given the high global burden of undiagnosed CKD, further studies are warranted to evaluate the clinical utility of ECG-based CKD screening.

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“…It has been reported AI algorithms can outperform human experts in detecting some ECG abnormalities. 5 11 However, it is unlikely that a human expert depends only on a single piece of information (e.g., initial ECG) when making a diagnosis of STEMI. Other information that might be considered includes vital signs, symptom description, and past medical history, as well as serial ECG measurements, echocardiogram, and even cardiac enzyme measurements.…”

Section: Introductionmentioning

confidence: 99%

A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department

et al. 2022

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Background Rapid revascularization is the key to better patient outcomes in ST-elevation myocardial infarction (STEMI). Direct activation of cardiac catheterization laboratory (CCL) using artificial intelligence (AI) interpretation of initial electrocardiography (ECG) might help reduce door-to-balloon (D2B) time. To prove that this approach is feasible and beneficial, we assessed the non-inferiority of such a process over conventional evaluation and estimated its clinical benefits, including a reduction in D2B time, medical cost, and 1-year mortality. Methods This is a single-center retrospective study of emergency department (ED) patients suspected of having STEMI from January 2021 to June 2021. Quantitative ECG (QCG™), a comprehensive cardiovascular evaluation system, was used for screening. The non-inferiority of the AI-driven CCL activation over joint clinical evaluation by emergency physicians and cardiologists was tested using a 5% non-inferiority margin. Results Eighty patients (STEMI, 54 patients [67.5%]) were analyzed. The area under the curve of QCG score was 0.947. Binned at 50 (binary QCG), the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 98.1% (95% confidence interval [CI], 94.6%, 100.0%), 76.9% (95% CI, 60.7%, 93.1%), 89.8% (95% CI, 82.1%, 97.5%) and 95.2% (95% CI, 86.1%, 100.0%), respectively. The difference in sensitivity and specificity between binary QCG and the joint clinical decision was 3.7% (95% CI, −3.5%, 10.9%) and 19.2% (95% CI, −4.7%, 43.1%), respectively, confirming the non-inferiority. The estimated median reduction in D2B time, evaluation cost, and the relative risk of 1-year mortality were 11.0 minutes (interquartile range [IQR], 7.3–20.0 minutes), 26,902.2 KRW (22.78 USD) per STEMI patient, and 12.39% (IQR, 7.51–22.54%), respectively. Conclusion AI-assisted CCL activation using initial ECG is feasible. If such a policy is implemented, it would be reasonable to expect some reduction in D2B time, medical cost, and 1-year mortality.

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Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation

Cited by 91 publications

References 29 publications

Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning

Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning

Deep learning based electrocardiographic screening for chronic kidney disease

A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department

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