An AI model using 12-lead ECG predicted pulmonary embolism with 100% specificity and an AUC of 0.75 (95% CI 0.66-0.82; p<0.001), performing significantly better than clinical prediction rules.
Observational (n=1,014)
Does a deep learning-based AI model using 12-lead ECG improve the diagnosis of acute pulmonary embolism compared to clinical prediction rules in emergency department patients with suspected pulmonary embolism?
A deep learning-based AI model using 12-lead ECG demonstrated high specificity and outperformed standard clinical prediction rules for diagnosing acute pulmonary embolism in emergency department patients.
Effect estimate: AUC 0.75 (95% CI 0.66-0.82)
p-value: p=<0.001
INTRODUCTION: Pulmonary embolism (PE) is a life-threatening condition, in which diagnostic uncertainty remains high given the lack of specificity in clinical presentation. It requires confirmation by computed tomography pulmonary angiography (CTPA). Electrocardiography (ECG) signals can be detected by artificial intelligence (AI) with precision. The purpose of this study was to develop an AI model for predicting PE using a 12-lead ECG. METHODS: We extracted 1014 ECGs from patients admitted to the emergency department who underwent CTPA due to suspected PE: 911 ECGs were used for development of the AI model and 103 ECGs for validation. An AI algorithm based on an ensemble neural network was developed. The performance of the AI model was compared against the guideline recommended clinical prediction rules for PE (Wells and Geneva scores combined with a standard D-dimer cut-off of 500 ng/mL and an age-adjusted cut-off, PEGeD and YEARS algorithm). RESULTS: The AI model achieves greater specificity to detect PE than the commonly used clinical prediction rules. The AI model shown a specificity of 100% (95% confidence interval (CI): 94-100) and a sensitivity of 50% (95% CI: 33-67). The AI model performed significantly better than the other models (area under the curve 0.75; 95% CI 0.66-0.82; p<0.001), which had nearly no discriminative power. The incidence of typical PE ECG features was similar in patients with and without PE. CONCLUSION: We developed and validated a deep learning-based AI model for PE diagnosis using a 12-lead ECG and it demonstrated high specificity.
Silva et al. (Wed,) conducted a observational in Suspected acute pulmonary embolism (n=1,014). Artificial intelligence model using 12-lead ECG vs. Clinical prediction rules (Wells, Geneva, PEGeD, YEARS) was evaluated on Prediction of pulmonary embolism (Area under the curve) (AUC 0.75, 95% CI 0.66-0.82, p=<0.001). An AI model using 12-lead ECG predicted pulmonary embolism with 100% specificity and an AUC of 0.75 (95% CI 0.66-0.82; p<0.001), performing significantly better than clinical prediction rules.