A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.

Abstract

Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases.

Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE.

Methods: Among patients who visited the emergency department between 2011 and 2019, PE cases were identified through a review of medical records. Non-PE ECGs were collected from patients without a diagnostic code for PE. There were 113 PE and 51,456 non-PE ECGs in the training and validation sets for developing the DLM, respectively, and 27 PE and 13,105 non-PE cases in an independent testing set for performance validation. A human-machine competition was conducted from the testing set to compare the performance of the DLM with that of physicians. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Survival analysis was used to assess the prognosis of the patients without PE, stratified by DLM prediction.

Results: The DLM was as effective as physicians in diagnosing PE, with 70.8% sensitivity and 69.7% specificity. The area under the ROC curve of DLM was 0.778 in the testing set and up to 0.9 with D-dimer and demographic data. The non-PE patients whose ECG was misclassified as PE by DLM had higher all-cause mortality [hazard ratio (HR) 2.13 (1.51-3.02)] and risk of non-cardiovascular hospitalization [HR 1.55 (1.42-1.68)] than those correctly classified.

Conclusions: A DLM-enhanced ECG system may prompt PE recognition and provide prognostic outcomes in patients with false-positive predictions.